How Can I Learn Data Science from Scratch?

What is Data Science?

Data science is a branch of science and it is a multidisciplinary field in which we use scientific methods, procedures, algorithms, and frameworks to find actionable insights or information from extremely large and complex data (Which can be either structural or non-structured).

It is a vast field that combines a wide range of disciplines, including computer science, programming, modelling, statistics, analytics, and math skills.

Last but not least, Data science involves recording, storing, and analyzing data to translate a particular organization's problems into research projects and then translate them into practical solutions.

Why is Data Science Important?

Without data analysis, it is hard for any organisation or corporation to compete in the market with superior data-driven choices, products, and services.

Because of this, the discipline of data science is expanding quickly, and there is greater need than ever before for data science specialists.

Experts in data science are those who understand how to harness the power of data to extract valuable information from unstructured data and set a business on the path to success.

The fact that there will be over 62% more data science employment in India alone in 2020 provides evidence of the relevance of data science across all industries.

There are several reasons why data science is important, some of which include the following:

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• Each day, billions of bytes of data are produced nowadays. Here, data science professionals step in and utilise the appropriate tools, methodologies, and algorithms to determine what we've done incorrectly in the past and what we should change going forward to satisfy customers.
• It benefits us to make machines more intelligent so that we can automate and improve upon human-related jobs that are now done manually.
• Data science allows us to better understand client behaviour so that we can subsequently give the appropriate product to the appropriate customer.
• Last but not least, data science aids in cost reduction, trend prediction, and the avertance of substantial financial losses.

What are the Key Components of Data Science?

Because data science is a vast field, it contains many components that we need to understand first before moving on the data science learning path. The basic components of data science are as follows:

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• Data

  A collection of unprocessed facts and figures, such as statements, observations, measurements, or simple descriptions of objects, are collectively referred to as data.

  If we take a closer look, we can see that the data may be classified as either organised or unstructured.

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  • Structured data

    Structure data is a type of data that is highly organized, formatted, searchable, and easily understood by machine language.

  • Unstructured data

    Unstructured data is the opposite of structured data and is unformatted, unorganized that isn't stored in a structured database format.

• Big Data

  Big data refers to extremely huge amounts of data that can only be calculated and assessed by computers, not by conventional data processing methods.

  It demands attention and is challenging to extract relationships, trends, and patterns because to its enormous volume.

  Here are some of the tools that data science experts use to handle big data:

  • Hadoop
  • Qubole
  • Apache Spark
  • Pig
  • Scala
  • Hive

• Machine Learning

  An essential component of data science, machine learning is a collection of algorithms that can analyse massive amounts of data on its own with little assistance from humans.

  We can make future forecasts, trend analyses, and situation-specific suggestions with the use of machine learning.

  • Linear Regression
  • Logistic Regression
  • Decision Tree
  • SVM
  • KNN
  • Naive Bayes

• Statistics and Probability

  Both components play an important role in solving data science-related problems. In addition to manipulating data to generate the required information, it provides us with a lot of information about statistics and independent and dependent variables, etc.

  Without proper knowledge of statistics and probability, anyone can make wrong decisions due to misinformation.

• Programming

        Data organisation, evaluation, investigation, and management are all done using programming languages. Data science simply needs to master two languages, Python and R, as opposed to learning several.

How to Learn Data Science from Scratch? – 3 Steps

Three Steps for Learning Data Science from Scratch

You likely already have a solid understanding of what data science is, why it's significant, and what the main facets of the discipline are.

It's time to return to the core subject and discover the procedures you must follow in order to properly study data science from beginning.

No matter if you are a beginner, intermediate, or experienced data scientist, by following the steps or strategy below, anyone may easily learn data science.

Let's study more about these procedures in depth so that you may begin your data science education from scratch.

• Learning Technical Skills

  Adopting technical skills is the first and foremost step in learning data science from scratch. If you successfully understand and learn technical skills, it will help you to understand more about algorithms with mathematics, which is a welcome thing.

  You don't have to learn every technical skill, just a basic understanding of some essential technical skills can help you stand out from the competition successfully.

  • Basic Programming with R & Python

    Whether you are interested in it or not, learning to programme is essential if you want to pursue a career in data science. Programming is a type of enjoyment.

    The bad news is that there are now around 700 programming languages available. The good news is that learning simply R and Python will be enough to land you a job.

    Prior to using Python and R to handle real-world data science challenges, it's vital to have a fundamental grasp of both languages.

    Both R and Python are open-source programming languages that are simple to learn; in fact, Python is the simplest language available. While both are crucial to data science, R is mostly used for statistical analysis.

    To simplify the data science process, many developers are putting a lot of effort into everyday creation of several new and intriguing libraries.

    • Numpy
    • Pandas
    • Matplotlib
    • Scikit-Learn
    • Seaborn
    • Scipy

    Once you have learned the fundamental packages and libraries of both languages, you can go ahead and learn the advanced features of R &Python.But it is clear that Python is more popular and it is often quoted more than R in job descriptions.

  • Learning Python for Data Analysis

    By now you should have a good idea of the basics of Python programming, now is the time to learn data analysis using Python.

    Udacity is a great place to learn data analysis without paying anything, and if you want to spend some money on learning, Coursera is another great platform that offers top-level data science courses.

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    But throughout the process, your main goal should be to focus on learning useful Python libraries such as Pandas and Numpy, which are essential for data analysis.

    You need to make sure that in the end, you are not only familiar with all the above-mentioned libraries but also with the data structures like Series, Arrays, and Data Frames.

    In addition to data structures and libraries, you should also be able to perform tasks such as sorting data, vectorized operations, grouping data, and collecting data from multiple files.

  • Machine Learning Using Python

    As the name implies, machine learning is the process by which systems or machines automatically learn from experience and improve to uncover hidden insights and predict future trends.

    One of the main tasks of someone in machine learning is to create models by developing new or using the predefined algorithms based on the type of data and business problem you are facing۔

    Python is used for this purpose because of its simplicity, consistency, flexibility, great libraries, platform independence, and a wide community.

    You can learn machine learning from many mediums, including YouTube, 360DiGiTMG, and Coursera, but once you learned, you should be able to understand the difference between Supervised Machine Learning and Unsupervised Machine Learning.

    Furthermore, try to learn in-depth the most popular and widely used algorithms, some of which are as follows.

    • Linear & Logistic Regression
    • Classification
    • Decision Trees
    • Random Forest
    • SVM
    • K-Means

  • SQL

    Aside from Python and R, SQL is another important and widely used programming language in data science. With the help of SQL, we can easily organize and access the data through the database. Therefore, it is an essential skill to fully learn data science.

• Statistics and Mathematics

  

  As we have discussed above, The field of data science deals with the conversion of raw and quantitative data into organized and informative information.

  And no doubt, you can't do that if you don't have a basic knowledge of statistics and math. You don't need a background in math or statistics, but you need to know a few basic things about statistics and math that will help you to understand many concepts, such as data distribution and algorithm work.

  So, let's first know how much math you need to learn to become a data science professional.

  • Linear Algebra

    Linear algebra is one of the most important topics to understand to learn data science from the ground.

    It is used in many things but is generally used for image recognition, image convolution, text analysis, and also image representation as tensors.

    It helps us design algorithms that can categorize different things.

    Here are the main topics to cover under linear algebra.

    • Matrix Transformations

      • Linear transformations
      • Inverse function
      • Transpose of a matrix
      • Multiplication of a matrix

    • Vectors and Space

      • Linear combinations
      • Vector dot and cross product
      • Vectors
      • Linear independence and dependence

  • Calculus

    Another important subject to learn in mathematics is calculus. Calculus is very important for gaining a deep understanding of machine learning and learning more about optimization techniques.

    This allows us to create different mathematical-based models to arrive at an optimal solution and also increase their accuracy and efficiency.

    Here are the main topics to cover under Calculus.

    • Integration and Differentiation

      • Primitive functions
      • Integration by substitution
      • The derivative of an indefinite integral
      • Integration by parts

    • Gradients

      • Directional derivatives
      • Integrals
      • Partial derivatives

    • Chain Rule

      • Composite functions
      • Multiple functions
      • Derivatives of composite functions

    Statistics is a branch of applied mathematics and is used not only to organize and use data but also to collect, analyze, and visualize quantitative data.

    Some of the important topics that lie under this umbrella are as follows:

    • Types of distribution, like Graphing, Summarizing, Sampling, and Normal Distributions, etc.
    • Probability
    • Research Design
    • Advanced Graphs
    • Estimation
    • Inference about slope
    • Regression
    • Central tendency
    • Summarization of data
    • Dependence measure
    • Analysis of Variance
    • Hypothesis testing
    • Significance Testing

• Practice with Projects

  

  Now that you have all the knowledge and the next big thing you need to do is practice as much as you can to maintain and enhance it.

  One of the best ways to do this is to work on different projects by solving business-related problems.

  If you are not able to find projects, then join any data science course at 360DigiTMG which offers many industries-based live projects and assignments.