Best Data Science Course : The Complete Guide for Beginners

A large number of employment vacancies exist in the burgeoning field of data science courses. The good news is that there are specialised training programmes available that make it feasible to pursue a career in data science regardless of one's educational background or level of experience. Statistics, mathematics, and computer science are all necessary for data science. Data Science demands a fundamental grasp of coding and programming. Among millennials, one of the top five job choices is data scientist. As data-driven judgements become more lucrative than feasible, their importance and demand are expected to increase in the future. Senior data scientists with 10 years of experience are paid between Rs. 20 and Rs. 30 per hour, while young data scientists with 5 years of experience are paid between Rs. 12 and Rs. 18 per hour. Freshmen with little to no experience may get a job as a data scientist for Rs 8–12 LPA. This training is in great demand across organisations and sectors, and there is a strong intake of applicants from different backgrounds who want to become data scientists. For anybody interested, studying data science offers a successful and rewarding career.

So how do we learn? That is the question that we ask or try to find answers about.

• Data Science Training near me

  Also, check this Data Science Institute in Bangalore to start a career in Data Science.

  Since 2012, professional training organizations or EdTech startups have been engaged in the delivery of Data Science programs in online and offline mode. Due to COVID 2019, primarily all programs have been delivered in online mode for the time being which is taught by universities across the globe. The majority of programs that are offered are master’s in Data Science. A few undergraduate programs are also available. However, the masters program is what undergraduates can go for in. Aspirants can choose from either of the two options to learn Data Science from. However, the university route demands higher time and financial investment. A lot of aspirants prefer to go to professional training and certification institutes, which also have credible universities and professional organizations accreditations. They are much quicker, cost-effective/cheaper, and help in a fast turnaround in an aspirant’s learning journey with visible and realizable impact. So, depending on the time and finance constraints, one can take an informed decision.

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  What is the Method to Study Data Science Training Program

  There is no shortage of training materials or curricula for data science. Education has become more democratic to the point that all information is readily and freely accessible. Aspirants might want to study the fundamentals independently. It is a good idea, but it won't last. An instructor always has a significant influence on the learning process of the aspirant. If one has to go further into the principles given, they can become a little serious and complicated. In order to avoid feeling overwhelmed by a torrent of information, teachers with real-world experience and practical training are required. Additionally, the teachers may guide the students in a time-efficient manner while they pursue information. Any competent and seasoned instructor will begin the course by outlining the CRISP-DM project management technique. You may read more about the CRISP-DM technique here (link). The methodology outlines each step that must be taken in order to complete a project. One must learn the ideas from start.

  

  • Understanding the Business Problem
  • Data Collection
  • Data Preparation
  • Data Mining
  • Model Evaluation
  • Model Deployment

• Data Science Course Requirements

  Can I have a successful career in this field? How can I fast pick up the essentials to land a job in data science? Hey, but what are the requirements for the programme? Am I even qualified to participate in this coaching? These are a few of the concerns an aspirant has when thinking about continuing. This is a really wise precaution to consider before making a choice. The majority of hopefuls choose their programmes based on criteria like the least expensive online Data Science degree or employment that are promised. Even before making that decision, one should thoroughly consider the course contents.

  Anyone can enter this field, it has been heavily advertised and pushed. This is accurate, however a potential candidate should be aware of what it takes to become a data scientist. When fancy-sounding phrases like technology, algorithm, machine learning, etc. are used, candidates frequently have their own interpretation of eligibility in mind, which prevents them from conducting an objective assessment. Instead of becoming lost in language, one should go deeper and comprehend more.

  Looking forward to becoming a Data Scientist? Check out the Data Science Course and get certified today.

  The easiest approach to judge one's eligibility is to first make a list of one's goals. Why would someone ever consider becoming a data scientist? Once the answer to that query is known, one may search for more comprehensive topics to study the basics. The element of coverage could take into account things like time availability, the needed financial commitment, educational background, professional job experience, and course work. Let's examine each of these elements individually.

  

  • Time: One can get into this field given that one is determined to commit and dedicate time every day for learning the fundamentals. Having said that, one also needs to decide whether one should do a full-time program through a university or one can go for short professional coaching. Time is of the essence and crucial for one’s success. One can succeed through any mode depending on their convenience. However, in the past success stories have majorly emerged through professional training institutions as universities across the globe have just started offering the Data Science syllabus.
  • Investment: We are a price-sensitive market and anything that sounds like a discount or cheapest draws our attention. A Data Science aspirant should not fall for such gimmicks. One needs to ensure the quality that one derives from the course curriculum. Hence, one can go for short term or long-term courses depending on investment available that could be diverted. Generally, the professional training institutes turn out to be extremely cost-effective, however, one needs to choose very cautiously.
  • Educational Background: There is an interesting trend that has emerged. There is more and more demand for talent from Social science and liberal arts backgrounds other than Applied Science and Engineering. So right from Liberal arts to Engineering sciences, there is a place for everyone.
  • Professional Work Experience: Previous work experience is highly valued in the field of Data Science. It is treated as domain expertise. Even an NGO activist can become a Data Scientist because he or she has a very good understanding of the social sector and data generated in the domain. So, when we develop AI solutions to curb human trafficking, such aspirants bring deep insights due to their domain expertise and add a lot of value because they also understand the modus operandi of such unlawful activities. This is just one scenario. Aspirants from a Fine Arts background have the power to disrupt the way visualization can be done using data. They can make it more engaging, and easy to interpret for making critical decisions. Also, people from technology and other sectors bring in their set of expertise to design new data-driven solutions.
  • Course work: Data Science is at the cross-section of Statistics, Mathematics, business, and Computer Science. Many aspirants freeze at the idea of Statistics, Mathematics, and coding. But not to worry. The level of concepts covered under Statistics and Mathematics is very basic and fundamental. Even aspirants from Social science and Liberal arts background can easily pick up the concepts. As far as coding is concerned, it is done using simple English language in open source tools such as R and Python. These are very user-friendly tools and extremely easy to learn. All the concepts taught work on these fundamental pillars.

  Mastering in Data Science tools and techniques will transform you into a professional Data Scientist. Let’s study the valuable insights to know what it takes to become a Successful Data Scientist.

• How to Become a Successful Data Scientist Training Online

  A Data Scientist will become one of the most important cornerstones of every industry, according to Thomas Davenport, who made this remark in a 2012 Harvard Business Review. For this career path, there has been no turning back since that time. According to reports, data scientists in the USA make more than $200,000 annually with five years of job experience. Organisations all across the world are having trouble locating the proper talent. The need for Data Scientists has risen dramatically.

  The market is in a frenzy as a result of the demand. Everyone wants a piece of this burgeoning industry, whether they have job experience or not. Regardless of their backgrounds, the majority of academics and professionals are upgrading their skills in order to take this course. We now think that everyone can earn a Master in Data Science thanks to the market's excitement. And it's accurate!! There is no shortage of people studying data science. This has given educational and affiliate companies a big boost.

  The market has been overrun by Data Science textbooks, tools, and syllabi in recent years. Every aspect of data science tells a compelling professional narrative. However, students and aspirants must first stand back and assess their prior academic training and professional experience. There are two categories of candidates' backgrounds: technical and non-technical. This is crucial since developing one's key competencies and abilities is necessary to pursue a career as a data scientist. This course's universe is vast, intricate, and always growing.

  The further one goes, the more intensive and technically advanced it becomes, eventually becoming more engineering focused. In order to obtain knowledge in the technical or non-technical stream, one can first choose and build the learning path. People with technical backgrounds, such as engineers, can grow expertise into more technology-oriented difficulties. People with backgrounds in the liberal arts or business might seek to building experience in addressing business challenges that could be quite statistically inclined.

  Learn the core concepts of Data Science Course video on YouTube:

  After deciding which stream to follow, the aspirant must carefully consider how much time can be dedicated to their schooling. Programmes in data science are available from several institutions and professional organisations. These courses cover a lot of ground.

  The global market for data science is slowly but surely maturing. The skill sets needed to become a Data Scientist have grown. Organisations now demand that data scientists not only be skilled at doing statistical analyses or developing machine learning models but also have knowledge of databases, cloud deployment, automation, and model scalability. Therefore, it is crucial to pick a programme that takes into account all the needs. Investigating pooled internet job marketplaces will allow one to learn more about the work position criteria. The job descriptions provide firsthand knowledge of what businesses now want of a data scientist. Organisations frequently request talents that are not necessary for the employment function in order to develop capacity for the future. An aspirant must pick a well curated programme that realistically addresses and satisfies the market need in order to live up to such expectations.

  Earn yourself a promising career in data science by enrolling in the Data Science Classes in Pune offered by 360DigiTMG.

  Organisations anticipate that Data Scientists will develop Artificial Intelligence by utilising their practical knowledge in fundamental statistics.

  Data Science learner needs to ensure that the course covers the below aspects:

  • Basic and inferential Statistics
  • Mathematical concepts (linear algebra and multivariate calculus)
  • Classical Machine Learning (supervised and unsupervised)
  • Artificial Intelligence (Deep Learning that involves neural networks)
  • Visualization and Reporting (using Tableau, QlikView, etc.)
  • Big Data Storage (using Hadoop, Hive, etc.)
  • Databases (Relational: SQL, MongoDB, Non-Relational: NoSQL, etc.)
  • Machine Learning on Cloud (AWS, Azure etc.)
  • Analytical tools (R, Python, Apache Spark, SAS, etc.)
  • Real-Time Data Handling (Apache Kafka, Amazon Kinesis, Flink, etc.)
  • Data Science Project Management Method (CRISP-DM)
  • Version control (Git and GitHub)

  A Data Scientist will be prepared with a solid foundation if they receive training in data science that covers all of these topics. Deep understanding of the fundamentals of statistics, mathematics, and machine learning algorithms is required, as well as extensive hands-on practise through several tasks related to each topic, in order to reinforce one's learning. One becomes better the more they practise. An aspirant data scientist should make sure the data science programme they choose will let them work on actual, live projects that can help them further their career and education. To create a fantastic portfolio to display, one has to work on several projects of this nature. Please avoid the error of constructing your portfolio with tried-and-true case studies like MNIST, Titanic, or Iris. Work on live hackathons organised by websites like Kaggle to gain additional practical experience. Make sure to post your profile on GitHub and Kaggle. It is crucial to create a LinkedIn professional networking profile that highlights your experience. Prospective Data Scientist job candidates are typically contacted by recruiters via professional networks.

  Although exceedingly challenging, the path to become a data scientist is also tremendously rewarding. Data scientists are highly recognised and respected in the market since they execute arduous work for organisations. The Data Scientists are given large pay checks as a sign of appreciation. In conclusion, the moment is excellent for becoming a data scientist, but one must be determined and committed to working hard.

  In conclusion, it is a terrific moment to be a data scientist, but one must have the fortitude and commitment to allow enough time for oneself to understand and practise the aforementioned ideas and techniques. The only way to excel as a data scientist is to follow that path.

• Data Science Course Modules

  This course espouses the CRISP-DM Project Management Methodology. A primer on statistics, DATA VISUALIZATION, plots, and Inferential Statistics, and Probability Distribution is contained in the premier modules of the course. The subsequent modules deal with Exploratory Data Analysis, Hypothesis Testing, and Data Mining Supervised Learning-enabled with Linear Regression and OLS. The following modules focus on the various regression models. We learn to enable Predictive Modeling with Multiple Linear Regression. The merits of Lasso and Ridge Regression, Logistic Regression, Multinomial Regression, and Advanced Regression For Count Data are explored. Data Mining Unsupervised Learning is the fulcrum of the next three modules. The various approaches used to enable the same like Clustering, Dimension Reduction, and Association Rules are elaborated in-depth with appropriate algorithms. The workings of Recommendation Engines and the key concepts of Network Analytics are also detailed.
  
  This Data Science Courses in India lends focus to Machine Learning algorithms like k-NN Classifier, Decision Tree and Random Forest, Ensemble Techniques- Bagging and Boosting, AdaBoost, Extreme Gradient Boosting, and Naive Bayes algorithm. Text Mining and Natural Language Processing also feature in the course curriculum. The building blocks of Neural Networks -ANN and Deep Learning Black Box Techniques like CNN, RNN, and SVM are also described in great detail. The concluding modules include model-driven and data-driven algorithm development for forecasting and Time Series Analysis. This is the most comprehensive course from the best data science training institute in India.

  1. CRISP – DM - Project Management Methodology

  Learn about insights on how data is assisting organizations to make informed data-driven decisions. Data is treated as the new oil for all the industries and sectors which keep organizations ahead in the competition. Learn the application of Big Data Analytics in real-time, you will understand the need for analytics with a use case. Also, learn about the best project management methodology for Data Mining - CRISP-DM at a high level.

   

  • All About 360DigiTMG & Innodatatics Inc., USA
  • Dos and Don'ts as a participant
  • Introduction to Big Data Analytics
  • Data and its uses – a case study (Grocery store)
  • Interactive marketing using data & IoT – A case study
  • Course outline, road map, and takeaways from the course
  • Stages of Analytics - Descriptive, Predictive, Prescriptive, etc.
  • Cross-Industry Standard Process for Data Mining

  2. Exploratory Data Analytics (EDA) / Descriptive Analytics

  Data Science project management methodology, CRISP-DM will be explained in this module in finer detail. Learn about Data Collection, Data Cleansing, Data Preparation, Data Munging, Data Wrapping, etc. Learn about the preliminary steps taken to churn the data, known as exploratory data analysis. In this module, you also are introduced to statistical calculations which are used to derive information from data. We will begin to understand how to perform a descriptive analysis.

   

  • Machine Learning project management methodology
  • Data Collection - Surveys and Design of Experiments
  • Data Types namely Continuous, Discrete, Categorical, Count, Qualitative, Quantitative and its identification and application
  • Further classification of data in terms of Nominal, Ordinal, Interval & Ratio types
  • Balanced versus Imbalanced datasets
  • Cross Sectional versus Time Series vs Panel / Longitudinal Data
  • Batch Processing vs Real Time Processing
  • Structured versus Unstructured vs Semi-Structured Data
  • Big vs Not-Big Data
  • Data Cleaning / Preparation - Outlier Analysis, Missing Values Imputation Techniques, Transformations, Normalization / Standardization, Discretization
  • Sampling techniques for handling Balanced vs. Imbalanced Datasets
  • What is the Sampling Funnel and its application and its components?
    • Population
    • Sampling frame
    • Simple random sampling
    • Sample
  • Measures of Central Tendency & Dispersion
    • Population
    • Mean/Average, Median, Mode
    • Variance, Standard Deviation, Range

  Learn the core concepts of Data Analytics Course video on Youtube:

  3. Statistical Data Business Intelligence and Data Visualization

  Learn about various statistical calculations used to capture business moments for enabling decision makers to make data driven decisions. You will learn about the distribution of the data and its shape using these calculations. Understand to intercept information by representing data by visuals. Also learn about Univariate analysis, Bivariate analysis and Multivariate analysis.

   

  • Measure of Skewness
  • Measure of Kurtosis
  • Spread of the Data
  • Various graphical techniques to understand data
    • Bar Plot
    • Histogram
    • Boxplot
    • Scatter Plot

  4. Plots & Inferential Statistics

  Data Visualization helps understand the patterns or anomalies in the data easily and learn about various graphical representations in this module. Understand the terms univariate and bivariate and the plots used to analyze in 2D dimensions. Understand how to derive conclusions on business problems using calculations performed on sample data. You will learn the concepts to deal with the variations that arise while analyzing different samples for the same population using the central limit theorem.

   

  • Line Chart
  • Pair Plot
  • Sample Statistics
  • Population Parameters
  • Inferential Statistics

  5. Probability Distributions (Continuous & Discrete)

  Want to learn more about data science? Enroll in the Best Data Science courses in Chennai to do so.

  In this tutorial you will learn in detail about continuous probability distribution. Understand the properties of a continuous random variable and its distribution under normal conditions. To identify the properties of a continuous random variable, statisticians have defined a variable as a standard, learning the properties of the standard variable and its distribution. You will learn to check if a continuous random variable is following normal distribution using a normal Q-Q plot. Learn the science behind the estimation of value for a population using sample data.

   

  • Random Variable and its definition
  • Probability & Probability Distribution
    • Continuous Probability Distribution / Probability Density Function
    • Discrete Probability Distribution / Probability Mass Function
  • Normal Distribution
  • Standard Normal Distribution / Z distribution
  • Z scores and the Z table
  • QQ Plot / Quantile - Quantile plot
  • Sampling Variation
  • Central Limit Theorem
  • Sample size calculator
  • Confidence interval - concept
  • Confidence interval with sigma
  • T-distribution / Student's-t distribution
  • Confidence interval
    • Population parameter with Standard deviation known
    • Population parameter with Standard deviation not known
  • A complete recap of Statistics

  6. Hypothesis Testing - The ‘4’ Must Know Hypothesis Tests

  Learn to frame business statements by making assumptions. Understand how to perform testing of these assumptions to make decisions for business problems. Learn about different types of Hypothesis testing and its statistics. You will learn the different conditions of the Hypothesis table, namely Null Hypothesis, Alternative hypothesis, Type I error and Type II error. The prerequisites for conducting a Hypothesis test, interpretation of the results will be discussed in this module.

   

  • Formulating a Hypothesis
  • Choosing Null and Alternative Hypothesis
  • Type I or Alpha Error and Type II or Beta Error
  • Confidence Level, Significance Level, Power of Test
  • Comparative study of sample proportions using Hypothesis testing
  • 2 Sample t-test
  • ANOVA
  • 2 Proportion test
  • Chi-Square test

  7. Data Mining Supervised Learning – Linear Regression, OLS

  Data Mining supervised learning is all about making predictions for an unknown dependent variable using mathematical equations explaining the relationship with independent variables. Revisit the school math with the equation of a straight line. Learn about the components of Linear Regression with the equation of the regression line. Get introduced to Linear Regression analysis with a use case for prediction of a continuous dependent variable. Understand about ordinary least squares technique.

   

  • Scatter diagram
    • Correlation analysis
    • Correlation coefficient
  • Ordinary least squares
  • Principles of regression
  • Simple Linear Regression
  • Exponential Regression, Logarithmic Regression, Quadratic or Polynomial Regression
  • Confidence Interval versus Prediction Interval
  • Heteroscedasticity / Equal Variance

  8. Predictive Modelling – Multiple Linear Regression

  In the continuation to Regression analysis study you will learn how to deal with multiple independent variables affecting the dependent variable. Learn about the conditions and assumptions to perform linear regression analysis and the workarounds used to follow the conditions. Understand the steps required to perform the evaluation of the model and to improvise the prediction accuracies. You will be introduced to concepts of variance and bias.

   

  • LINE assumption
    • Linearity
    • Independence
    • Normality
    • Equal Variance / Homoscedasticity
  • Collinearity (Variance Inflation Factor)
  • Multiple Linear Regression
  • Model Quality metrics
  • Deletion Diagnostics

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  9. Lasso and Ridge Regressions

  Learn about overfitting and underfitting conditions for prediction models developed. We need to strike the right balance between overfitting and underfitting, learn about regularization techniques L1 norm and L2 norm used to reduce these abnormal conditions. The regression techniques Lasso and Ridge techniques are discussed in this module .

   

  • Understanding Overfitting (Variance) vs. Underfitting (Bias)
  • Generalization error and Regularization techniques
  • Different Error functions or Loss functions or Cost functions
  • Lasso Regression
  • Ridge Regression

  10. Logistic Regression – Binary Value Prediction, MLE

  You have learnt about predicting a continuous dependent variable. As part of this module, you will continue to learn Regression techniques applied to predict attribute Data. Learn about the principles of the logistic regression model, understand the sigmoid curve, the usage of cutoff value to interpret the probable outcome of the logistic regression model. Learn about the confusion matrix and its parameters to evaluate the outcome of the prediction model. Also, learn about maximum likelihood estimation.

   

  • Principles of Logistic regression
  • Types of Logistic regression
  • Assumption & Steps in Logistic regression
  • Analysis of Simple logistic regression results
  • Multiple Logistic regression
  • Confusion matrix
    • False Positive, False Negative
    • True Positive, True Negative
    • Sensitivity, Recall, Specificity, F1
  • Receiver operating characteristics curve (ROC curve)
  • Precision Recall (P-R) curve
  • Lift charts and Gain charts

  11. Multinomial Regression

  Learn the core concepts of Data Science Course video on YouTube:

  Extension to logistic regression We have a multinomial regression technique used to predict a multiple categorical outcome. Understand the concept of multi logit equations, baseline and making classifications using probability outcomes. Learn about handling multiple categories in output variables including nominal as well as ordinal data.

   

  • Logit and Log-Likelihood
  • Category Baselining
  • Modeling Nominal categorical data
  • Handling Ordinal Categorical Data
  • Interpreting the results of coefficient values

  12. Advanced Regression for Count Data

  As part of this module you learn further different regression techniques used for predicting discrete data. These regression techniques are used to analyze the numeric data known as count data. Based on the discrete probability distributions namely Poisson, negative binomial distribution the regression models try to fit the data to these distributions. Alternatively, when excessive zeros exist in the dependent variable, zero-inflated models are preferred, you will learn the types of zero-inflated models used to fit excessive zeros data.

   

  • Poisson Regression
  • Poisson Regression with Offset
  • Negative Binomial Regression
  • Treatment of data with Excessive Zeros
    • Zero-inflated Poisson
    • Zero-inflated Negative Binomial
    • Hurdle Model

  13. Machine Learning - k -NN Classifier

  k Nearest Neighbor algorithm is distance based machine learning algorithm. Learn to classify the dependent variable using the appropriate k value. The k-NN classifier also known as lazy learner is a very popular algorithm and one of the easiest for application.

   

  • Deciding the K value
  • Thumb rule in choosing the K value
  • Building a KNN model by splitting the data
  • Checking for Underfitting and Overfitting in KNN
  • Generalization and Regulation Techniques to avoid overfitting in KNN

  14. Decision Tree & Random Forest

  Decision Tree & Random forest are some of the most powerful classifier algorithms based on classification rules. In this tutorial, you will learn about deriving the rules for classifying the dependent variable by constructing the best tree using statistical measures to capture the information from each of the attributes. Random forest is an ensemble technique constructed using multiple Decision trees and the final outcome is drawn from the aggregating the results obtained from these combinations of trees.

   

  • Elements of classification tree - Root node, Child Node, Leaf Node, etc.
  • Greedy algorithm
  • Measure of Entropy
  • Attribute selection using Information gain
  • Ensemble techniques - Stacking, Boosting and Bagging
  • Decision Tree C5.0 and understanding various arguments
  • Checking for Underfitting and Overfitting in Decision Tree
  • Generalization and Regulation Techniques to avoid overfitting in Decision Tree
  • Random Forest and understanding various arguments
  • Checking for Underfitting and Overfitting in Random Forest
  • Generalization and Regulation Techniques to avoid overfitting in Random Forest

  15. Ensemble Techniques - Bagging and Boosting

  Learn about improving reliability and accuracy of decision tree models using ensemble techniques. Bagging and Boosting are the go to techniques in ensemble techniques. The parallel and sequential approaches taken in Bagging and Boosting methods are discussed in this module.

   

  • Overfitting
  • Underfitting
  • Pruning
  • Boosting
  • Bagging or Bootstrap aggregating

  16. AdaBoost & Extreme Gradient Boosting

  The Boosting algorithms AdaBoost and Extreme Gradient Boosting are discussed as part of this continuation module You will also learn about stacking methods. Learn about these algorithms which are providing unprecedented accuracy and helping many aspiring data scientists win the first place in various competitions such as Kaggle, CrowdAnalytix, etc.

  • AdaBoost / Adaptive Boosting Algorithm
  • Checking for Underfitting and Overfitting in AdaBoost
  • Generalization and Regulation Techniques to avoid overfitting in AdaBoost
  • Gradient Boosting Algorithm<
  • Checking for Underfitting and Overfitting in Gradient Boosting
  • Generalization and Regulation Techniques to avoid overfitting in Gradient Boosting
  • Extreme Gradient Boosting (XGB) Algorithm
  • Checking for Underfitting and Overfitting in XGB
  • Generalization and Regulation Techniques to avoid overfitting in XGB

  17. Text Mining and Natural Language Processing (NLP)

  Learn to analyse the unstructured textual data to derive meaningful insights. Understand the language quirks to perform data cleansing, extract features using a bag of words and construct the key-value pair matrix called DTM. Learn to understand the sentiment of customers from their feedback to take appropriate actions. Advanced concepts of text mining will also be discussed which help to interpret the context of the raw text data. Topic models using LDA algorithm, emotion mining using lexicons are discussed as part of NLP module.

   

  • Sources of data
  • Bag of words
  • Pre-processing, corpus Document Term Matrix (DTM) & TDM
  • Word Clouds
  • Corpus level word clouds
    • Sentiment Analysis
    • Positive Word clouds
    • Negative word clouds
    • Unigram, Bigram, Trigram
  • Semantic network
  • Clustering
  • Extract user reviews of the product/services from Amazon, Snapdeal and trip advisor
  • Install Libraries from Shell
  • Extraction and text analytics in Python
  • LDA / Latent Dirichlet Allocation
  • Topic Modelling
  • Sentiment Extraction
  • Lexicons & Emotion Mining

  18. Machine Learning Classifier Technique - Naive Bayes

  Revise Bayes theorem to develop a classification technique for Machine learning. In this tutorial you will learn about joint probability and its applications. Learn how to predict whether an incoming email is a spam or a ham email. Learn about Bayesian probability and the applications in solving complex business problems.

   

  • Probability – Recap
  • Bayes Rule
  • Naïve Bayes Classifier
  • Text Classification using Naive Bayes
  • Checking for Underfitting and Overfitting in Naive Bayes
  • Generalization and Regulation Techniques to avoid overfitting in Naive Bayes

  19. Introduction to Perceptron and Multilayer Perceptron

  Perceptron algorithm is defined based on a biological brain model. You will talk about the parameters used in the perceptron algorithm which is the foundation of developing much complex neural network models for AI applications. Understand the application of perceptron algorithms to classify binary data in a linearly separable scenario.

   

  • Neurons of a Biological Brain
  • Artificial Neuron
  • Perceptron
  • Perceptron Algorithm
  • Use case to classify a linearly separable data
  • Multilayer Perceptron to handle non-linear data

  20. Building Blocks of Neural Network - ANN

  Neural Network is a black box technique used for deep learning models. Learn the logic of training and weights calculations using various parameters and their tuning. Understand the activation function and integration functions used in developing a neural network.

   

  • Integration functions
  • Activation functions
  • Weights
  • Bias
  • Learning Rate (eta) - Shrinking Learning Rate, Decay Parameters
  • Error functions - Entropy, Binary Cross Entropy, Categorical Cross Entropy, KL Divergence, etc.

  21. Deep Learning Primer

  • Artificial Neural Networks
  • ANN Structure
  • Error Surface
  • Gradient Descent Algorithm
  • Backward Propagation
  • Network Topology
  • Principles of Gradient Descent (Manual Calculation)
  • Learning Rate (eta)
  • Batch Gradient Descent
  • Stochastic Gradient Descent
  • Minibatch Stochastic Gradient Descent
  • Optimization Methods: Adagrad, Adadelta, RMSprop, Adam
  • Convolution Neural Network (CNN)
    • ImageNet Challenge – Winning Architectures
    • Parameter Explosion with MLPs
    • Convolution Networks
  • Recurrent Neural Network
    • Language Models
    • Traditional Language Model
    • Disadvantages of MLP
    • Back Propagation Through Time
    • Long Short-Term Memory (LSTM)
    • Gated Recurrent Network (GRU)

  22. Kernel Method - SVM

  • Support Vector Machines / Large-Margin / Max-Margin Classifier
  • Hyperplanes
  • Best Fit "boundary"
  • Linear Support Vector Machine using Maximum Margin
  • SVM for Noisy Data
  • Non- Linear Space Classification
  • Non-Linear Kernel Tricks
    • Linear Kernel
    • Polynomial
    • Sigmoid
    • Gaussian RBF
  • SVM for Multi-Class Classification
    • One vs. All
    • One vs. One
  • Directed Acyclic Graph (DAG) SVM

  23. Data Mining Unsupervised Learning – Clustering

  Data mining unsupervised techniques are used as EDA techniques to derive insights from the business data. In this first module of unsupervised learning, get introduced to clustering algorithms. Learn about different approaches for data segregation to create homogeneous groups of data. Hierarchical clustering, K means clustering are most commonly used clustering algorithms. Understand the different mathematical approaches to perform data segregation. Also learn about variations in K-means clustering like K-medoids, K-mode techniques, learn to handle large data sets using CLARA technique.

   

  • • Hierarchical • Supervised vs Unsupervised learning • Data Mining Process • Hierarchical Clustering / Agglomerative Clustering • Dendrogram • Measure of distance
    • Numeric
      • Euclidean, Manhattan, Mahalanobis
    • Categorical
      • Binary Euclidean
      • Simple Matching Coefficient
      • Jaquard's Coefficient
    • Mixed
      • Gower's General Dissimilarity Coefficient
    • Types of Linkages
      • Single Linkage / Nearest Neighbour
      • Complete Linkage / Farthest Neighbour
      • Average Linkage
      • Centroid Linkage
    • K-Means Clustering
      • Measurement metrics of clustering
        • Within the Sum of Squares
        • Between the Sum of Squares
        • Total Sum of Squares
      • Choosing the ideal K value using Scree Plot / Elbow Curve
      • Other Clustering Techniques
        • K-Medians
        • K-Medoids
        • K-Modes
        • Clustering Large Application (CLARA)
        • Partitioning Around Medoids (PAM)
        • Density-based spatial clustering of applications with noise (DBSCAN)

  24. Data Mining Unsupervised Learning - Dimension Reduction (PCA)

  Dimension Reduction (PCA) / Factor Analysis Description: Learn to handle high dimensional data. The performance will be hit when the data has a high number of dimensions and machine learning techniques training becomes very complex, as part of this module you will learn to apply data reduction techniques without any variable deletion. Learn the advantages of dimensional reduction techniques. Also, learn about yet another technique called Factor Analysis.

   

  • Why Dimension Reduction
  • Advantages of PCA
  • Calculation of PCA weights
  • 2D Visualization using Principal components
  • Basics of Matrix Algebra
  • Factor Analysis

  25. Data Mining Unsupervised Learning - Association Rules

  Learn to measure the relationship between entities. Bundle offers are defined based on this measure of dependency between products. Understand the metrics Support, Confidence and Lift used to define the rules with the help of Apriori algorithm. Learn pros and cons of each of the metrics used in Association rules.

   

  • What is Market Basket / Affinity Analysis
  • Measure of Association
    • Support
    • Confidence
    • Lift Ratio
  • Apriori Algorithm
  • Sequential Pattern Mining

  26. Recommendation Engine

  Personalized recommendations made in e-commerce are based on all the previous transactions made. Learn the science of making these recommendations using measuring similarity between customers. The various methods applied for collaborative filtering, their pros and cons, SVD method used for recommendations of movies by Netflix will be discussed as part of this module.

   

  • User-based Collaborative Filtering
  • A measure of distance/similarity between users
  • Driver for Recommendation
  • Computation Reduction Techniques
  • Search based methods/Item to Item Collaborative Filtering
  • SVD in recommendation
  • The vulnerability of recommendation systems

  27. Network Analytics

  Study of a network with quantifiable values is known as network analytics. The vertex and edge are the node and connection of a network, learn about the statistics used to calculate the value of each node in the network. You will also learn about the google page ranking algorithm as part of this module.

   

  • Definition of a network (the LinkedIn analogy)
  • The measure of Node strength in a Network
    • Degree centrality
    • Closeness centrality
    • Eigenvector centrality
    • Adjacency matrix
    • Betweenness centrality
    • Cluster coefficient
  • Introduction to Google page ranking

  28. Auto Machine Learning (Auto ML)

  • AutoML Methods
  • AutoML Systems
  • AutoML on Cloud - AWS
    • Amazon SageMaker
    • Sagaemaker Notebook Instance for Model Development, Training and
    • Deployment
    • XG Boost Classification Model
    • Hyperparameter tuning jobs
  • AutoML on Cloud - Azure
    • Workspace
    • Environment
    • Compute Instance
    • Automatic Featurization
    • AutoML and ONNX
  • AutoML on Cloud - GCP
    • AutoML Natural Language Performing Document Classification
    • Performing Sentiment Analysis using AutoML Natural Language API
    • Cloud ML Engine and Its Components
    • Training and Deploying Applications on Cloud ML Engine
    • Choosing Right Cloud ML Engine for Training Jobs

  29. Survival Analytics

  Kaplan Meier method and life tables are used to estimate the time before the event occurs. Survival analysis is about analyzing this duration or time before the event. Real-time applications of survival analysis in customer churn, medical sciences and other sectors is discussed as part of this module. Learn how survival analysis techniques can be used to understand the effect of the features on the event using Kaplan Meier survival plot.

   

  • Examples of Survival Analysis
  • Time to event
  • Censoring
  • Survival, Hazard, Cumulative Hazard Functions
  • Introduction to Parametric and non-parametric functions

  30. Forecasting/Time Series – Model-Driven Algorithms

  Time series analysis is performed on the data which is collected with respect to time. The response variable is affected by time. Understand the time series components, Level, Trend, Seasonality, Noise and methods to identify them in a time series data. The different forecasting methods available to handle the estimation of the response variable based on the condition of whether the past is equal to the future or not will be introduced in this module. In this first module of forecasting, you will learn the application of Model-based forecasting techniques.

   

  • Introduction to time series data
  • Steps to forecasting
  • Components to time series data
  • Scatter plot and Time Plot
  • Lag Plot
  • ACF - Auto-Correlation Function / Correlogram
  • Visualization principles
  • Naïve forecast methods
  • Errors in the forecast and it metrics - ME, MAD, MSE, RMSE, MPE, MAPE
  • Model-Based approaches
    • Linear Model
    • Exponential Model
    • Quadratic Model
    • Additive Seasonality
    • Multiplicative Seasonality
  • Model-Based approaches Continued
  • AR (Auto-Regressive) model for errors
  • Random walk

  31. Forecasting/Time Series - Data-Driven Algorithms

  In this continuation module of forecasting learn about data-driven forecasting techniques. Learn about ARMA and ARIMA models which combine model-based and data-driven techniques. Understand the smoothing techniques and variations of these techniques. Get introduced to the concept of de-trending and deseasonalize the data to make it stationary. You will learn about seasonal index calculations which are used for reseasonalize the result obtained by smoothing models.

   

  • ARMA (Auto-Regressive Moving Average), Order p and q
  • ARIMA (Auto-Regressive Integrated Moving Average), Order p, d, and q
  • A data-driven approach to forecasting
  • Smoothing techniques
    • Moving Average
    • Exponential Smoothing
    • Holt's / Double Exponential Smoothing
    • Winters / Holt-Winters
  • De-seasoning and de-trending
  • Econometric Models
  • Forecasting using Python
  • Forecasting using R

• Why is the world giving much emphasis on Data Science? How Best Data Science Certificate Programs Will Change the world?

  Data science has had a major impact on the globe. Data science has made a significant influence everywhere, including in the reduction of human trafficking, early autism diagnosis, the battle against global warming, and the development of sustainable corporate impact through wise decision-making. And these are only a few instances from different fields. The advantages are always changing. As a tool for commercial success as well as for the broader welfare of society, its importance has grown. Let's attempt to comprehend the true reason for its requirement.

  The primary duty is to examine the data, regardless of the subject matter, in order to draw patterns and insights from it. The scope entails defining and comprehending the current issue, documenting the goal and constraints of the problem. Then, depending on availability, the pertinent data is gathered from secondary or primary sources. Data scientists must generally work around the untidy format of the data that has been acquired. The data will be sorted and pre-processed by the data scientist so that it is ready for analysis in a clear and structured manner. After carefully examining the data, the data scientist uses it to make predictions that are utilised by businesses to develop a strategy to increase profits, by the healthcare industry to identify and prevent health problems, by social scientists to comprehend how people behave, etc. Simply put, make wiser, more informed, more thoughtful judgements.

  Since social media outlets opened up, there has been a data explosion. Data is provided in a variety of formats, including text, audio, and video. Exabytes are units of data generation where one exabyte is equal to 100,000 gigabytes. The era of big data and artificial intelligence was therefore born. And the requirement for using the data has multiplied. Whether it's a corporation, a government, an intelligence agency, a hospital, a not-for-profit organisation, etc. Numerous chances and possibilities have been created by data. It's interesting to note that previous statisticians would analyse data and aid organisations in improving. But when the cost of storing data dropped, cloud platforms allowed for more storage, and computing power rose, other information streams than just statistics were used, giving rise to the development of data science engineering methodologies.

  Data Science therefore gained popularity as a stunning combination of computers and statistics. We can manually only model a small number of situations for any given issue, but this added capacity allows us to simulate scenarios with countless dimensions. The breadth is so broad that through supporting innovation, it is assisting our development and helping us create a better future. It enables us to create cutting-edge, ergonomic goods that provide long-term solutions for future generations. We do not dismiss the existence of additional information streams that support such advancements. However, whether the data is organised or unstructured, qualitative or quantitative, etc., it must always be reviewed at first.

  Making computers acquire empathy will be very difficult, according to Jack Ma, the founder of Alibaba, the largest online retailer in China, who made this statement in an interview. Well, we concur! The power of data science and anthropology is being used by organisations like Quilt.AI to scale up the development of human empathy in robots. Recently, researchers studied young males under the age of 18 in the state of Rajasthan to better understand "facets of masculinity" and how these facets shape their behaviour towards women. They are starting a behaviour modification campaign to stop online eve-teasing and sexist behaviour utilising the data insights they obtained. Sophisticated methods utilising Deep Learning algorithms and computer vision have made it feasible to collect property taxes.

  Data science is a very effective method for finding patterns in data and using those patterns to solve complicated issues. Businesses utilise it to improve customer experience, develop better marketing plans, boost operational efficiency, etc. Governments use them to improve governance and create long-lasting public policy. The healthcare sector also uses data to comprehend human health in order to assist individuals in better managing their health. The foundation of the world has evolved into this. And it will undoubtedly improve the lives of us and future generations.

• Benefits of Data Science for Business

  The biggest benefactors of Data Science are businesses. Most of the groundbreaking innovations have been reported in the business domain. One of the primary reasons is that over the period the cost of storing and managing data has substantially reduced. Hence, the organizations can store the generated data and take benefit from it with the help of Data Scientist. The scope is very wide as the domain is very versatile. It can be easily integrated and implemented in any business scenario based on the availability of data. So, let us understand why Data Science is needed and how it can benefit a business.

  The components include defining the business problem, collecting the relevant data, cleaning and preprocessing the data, driving insights, building predictive models, and using the predictions to design business strategy to drive maximum mileage out of it. This methodology is simple and easily implemented. However, every step of the method has its challenges and takes on those challenges head-on. Every department of the business generates a lot of data and with the help of the Data Science process, it is put to best use to positively impact the top and bottom line. We can try to understand the value derived by looking at a few business departments.

  

  • Sales - It is one of the most crucial company sectors since it serves as a centre for direct income generating. The sales team goes out into the market every day to connect with potential and current customers in order to offer the company's services and goods. Data science aids in the development and conversion of high-quality leads. Additionally, customer replies for queries posed by potential customers during the pre-sale stage are automated using advanced techniques like Natural Language Processing. This aids in the customer onboarding process early on. This implies that by speaking with more clients and so growing the business volume, the sales staff may do more.
  • Marketing – A business can be in the distribution of multiple products. In real-time, it is quite tedious to match the right products to personalized customer needs. With the help of Data Science, the marketing team can mine the historical data for patterns for consumer needs and identify cross-sell opportunities. This helps in addressing the needs of that segment of customers as well, who have not been engaged regularly by the business. This reduces customer churn, builds customer loyalty, and increases customer lifetime value for the business.
  • Human Resource – Analytics for the workforce is a result of data science. The business's human resources division gains a lot from this. Talent acquisition has always been a challenging and time-consuming task when it comes to managing employee perks and compensation. With the help of workforce analytics, inertia has been eliminated in the process of finding the right personnel for the job and determining the appropriate amount of remuneration. The data overload produced by candidate resumes for a job vacancy may be cleaned up by human resource teams using bots. The bot correctly separates and selects candidates with the appropriate skill set. Additionally, by examining employee behaviour and conduct, workforce analytics allows for the control of employee attribution since it provides early warning indicators.
  • Operations – The back office of any business is the backbone of all activities critical for a service or product delivery. Many times a business can claim that it is extremely difficult to track the progress of work. Like in infrastructure projects such as Real estate, tracking daily developments is a real task and challenging to define. To solve this issue, Data Scientists place multiple sensors in the safety wear of the construction staff to get the real-time feed of data to track task completion daily. Using computer vision, Data Scientists help the operations team to maintain optimum quality in production lines across manufacturing and services.
  • Treasury – A company's Treasury department is alone in charge of handling cash management and investments. Additionally, it guarantees that the company may increase its bottom line through treasury operations. This is complicated by businesses' ambiguous usage of cash and the unknowable cost of banking. To determine the actual cash required by various business units, the treasury department can examine historical data on payments and receivables combined with expected values. When comparing bank statements with transaction data, the treasurer uses data tools to detect high-cost scenarios and prevent cash loss.
  • Security – Irrespective of the size of the business, it has to ensure the security of its premises and employees. It is responsible to curb any unauthorized access and secure employees from any instance of physical harm. Using computer vision and Deep Learning algorithm, tracking and self-operating devices can be deployed that use facial recognition to identify employees and related staff from outsiders to control access and also to track the physical location of employees on business premises to rescue in case of any unforeseen event.

  These are just a handful of uses of Data for business. If we look at the business from the perspective of supply chain wise or value chain wise, then we can additionally unearth innumerable benefits of Data Science for any business. What is Data Analytics? Is it similar or different from Data Science? These topics are discussed in detail below.

• Difference between Data Science and Analytics

  Data literacy is essential in the data-driven economy. To be competitive in the job market of the future, everyone is scrambling to sharpen their data abilities. But more often than not, people struggle with the decision of choosing between a Data Analytics or Data Science career. Let's first examine how to distinguish between the Data Analytics definition and the Data Science definition in order to resolve the conundrum. Read a piece about the future of data scientists.

  They're both reticular in structure. The two, however, use completely different strategies and have very different outcomes. To address the questions we are aware of, data analytics focuses on descriptive and diagnostic features. What occurred, and why did it occur? The benefit of analytics is that by arranging, analysing, and presenting the data in the best way possible, we can provide insights that can be put into practise. It is possible to quickly enhance operations by implementing the insights obtained from data analytics approaches. Data exploration and analysis are done using statistical approaches. It just serves as a foundation for data science.

  A combination of statistics and machine learning is data science. A data scientist uses a variety of techniques to analyse vast amounts of data to find solutions to issues that were previously unsolvable. Analytics that are predictive and prescriptive are two benefits of data science. Finding the correct questions to ask, rather than providing incisive answers, is the main goal of the data scientist. To improve the analysis and forecasting of possible trends, the data scientist explores diverse and fragmented data. The Data Scientist goes above and beyond by prescribing actions based on the derived projections for long-term strategic planning. This strengthens the importance of data science.

  

  The pay demanded by data analysts and data scientists are significantly different because of the fundamental differences between data analytics and data science. The wages start at about INR 4.5 lakhs annually. Freshmen may earn between INR 4.5 lakhs and INR 12.5 lakhs annually, but experienced career changers may earn between INR 25 lakhs and INR 30 lakhs annually. Data analytics salaries for new hires vary from INR 1.7 lakhs to INR 6.5 lakhs annually, while they range from INR 8.5 lakhs to INR 20 lakhs annually for seasoned specialists. The intricacy of the work carried out by a Data Scientist vs a Data Analyst determines how the two streams are valued. All of the essential duties of a data analyst are covered by a data scientist, but they go far beyond and contribute additional value. However, no candidate should use pay as a criterion while choosing between the two knowledge streams. A rigorous assessment of one's skill set and motivation should be done before deciding whether to pursue a career as a Data Analyst or a Data Scientist.

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