Data Science Tutorials: Learn Data Science from Scratch

WHAT AND WHY

The world, at large, has become a global village of sorts with the rise of the internet. This proliferation of internet space has also caused a rise in the increase in available data. This rise in the amount of data available came with an initial problem which was storage. The drive of technologically inclined companies, then, was to be able to build something that would be perfect storage for a large amount of this data. Over time, this issue of storage has been resolved but it begs the question, what are we to do with so much data? This is where Data Science comes in.

Prior to today, the main software programmes utilised to cater to information and their analysis were business intelligence products. But there has recently been somewhat of an information explosion. This amount of data is not presented in compact or organised ways. Instead, you have a tonne of raw data from sources like social media feeds, blogs, security cameras, sensors, and multimedia files, among others.

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

In these forms, there is hardly anything that can be done on these data with Business Intelligence tools. With Data Science, there are better tools able to handle huge amounts of data from all kinds of sources and use them to solve problems and carry out innovations.

DATA SCIENCE: A HISTORY

More partnerships resulted from this thesis, and eventually the International Association for Statistical Computing (IASC) was founded. This was the first significant development, fusing the concepts of computers and statistics with the intention of gathering data and turning it into information. Another work by Turkey, titled Exploratory Data Analysis, was published in 1977. In the essay, he makes the argument that both confirmatory and exploratory data analysis should be compatible with one another.

The inaugural workshop for the ACM SIGKDD Conference on Knowledge Discovery and Data Mining took place in 1989. This gathered authors and thinkers from several disciplines together in one location for discussions and practicals on the potential of data science.

DATA SCIENCE: THE FUTURE

According to Kenneth Cukier in a write-up published in The Economist (2010), Data Scientists are able to fuse the abilities of a programmer, artist, and statistician to bring out the value within raw volumes of confusing data. Since its inception, Data Science has managed to grow into an even bigger field and saturate a large number of other fields of study which need data to function. Which field doesn't? Thus, Data Scientists are needed now more than ever in different fields of endeavour.

Nevertheless, there are professionals in the field who believe that the quality and quantity of good data scientists capable of handling the kind of data existent now are at an all-time low. For example, Half Varian, a Google Chief Economist stated that the lack of such sets of people with these skills could lead to a future where these skills would be even of more value as the rate of data existence increases.

COMPONENTS OF DATA SCIENCE

There are building blocks that make up this field of study called Data Science. In order to better understand Data Science, one must first be acquainted with these. For example:

Data Science Tools

Data Science: Methodology

We would go into the techniques used in data science to comprehend data and find solutions in this section of the lesson.

1. Problem to Approach
2. Requirements to Collection
3. Understanding to Preparation
4. Modelling to Evaluation
5. Deployment to Feedback

1. PROBLEM TO APPROACH METHODOLOGY

   Here, the objective is to understand the issue through the information at hand and then utilise that information to identify a solution.

2. REQUIREMENT TO COLLECTION METHODOLOGY

   Since the problem is known, the focus is on what the problem requires for it to be solved. The data requirements are then listed and once this is done, the data scientist goes to pick (collect) from the available data to solve the problem.

3. UNDERSTANDING TO PREPARATION METHODOLOGY

   The majority of the necessary data have already been gathered, and it is now being evaluated and reported using descriptive statistics and mathematical operations like mean, median, and mode. After that, the data is cleaned up and checked for errors such as erroneous or missing data before being prepared for modelling. To prevent having to start over, this step is vital and should be properly completed.

4. MODELLING TO EVALUATION METHODOLOGY

   At this stage, the focus is on building models that would either be descriptive or predictive with the existing data. Descriptive models help especially in recommendation while predictive models excel at telling the foreseeable future based on existing data. Thereafter, the models are then evaluated and tested to make sure it is solving the issues it was created for. Evaluation is used to do an appraisal of the model.

5. DEPLOYMENT TO FEEDBACK METHODOLOGY

   The model is delivered for final testing when it has been evaluated and the data scientist is satisfied with it. Following this deployment, customers provide input on whether they think the work was done well or where it might have been done better.

6. DATA SCIENCE: ADVANTAGES

   • Increased Demand: The rise of the Internet and technology is creating a larger influx of data. Thus, Data Scientists are in high demand. Different fields from healthcare to banking and finance, to SMEs, amongst others, are making more use of data and are in need of professionals in this field.
   • Better Healthcare: Machine Learning and other Data Science components are making healthcare more precise and capable in diagnosis and the best steps to take. The struggle against viral outbreaks, pandemics, and cancer, could have remedies in the future with the help of Data Science.
   • Improved Customer Experience: With the help of Data Science, product recommendations are more precise and customized to the whims and likes of clients.

7. DATA SCIENCE: DISADVANTAGES

   • Overreliance on Data: Although Data Science makes for more data, it cannot guarantee such data. This implies that data may be employed despite being unverified and provide unexpected consequences.
   • Privacy Issues: The problem of privacy poses a significant concern to any businesses using data. Despite being valuable for businesses, data may contain information that a customer wants to remain private. The security of this sensitive information is more in jeopardy due to the rising allegations of breaches and online deception.
   

8. DATA SCIENCE: APPLICATIONS

   • Prediction: It takes data from places like ships, satellites, aircraft, buoys, and land stations to predict weather conditions and imminent natural disasters such as storms, earthquakes, and floods, to mention but a few. The idea is to make use of these data to know when to expect some of these things which could have far-reaching effects on the business and its profitability, diminishing the amount of risk and damage that would have been incurred.
   • Product Recommendation: Conventional business methods provided suggestions based on information from browsers and customer purchase histories. However, via the use of data science, more information collected from a wider range of sources, such as social media, likes, comments, and frequently visited websites, may provide more accurate product suggestions. This greatly enhances and maximises the consumer experience.
   • Control: Furthermore, it offers a larger perspective through which Artificial Intelligence devices, without human input, can make more informed decisions and choices that would be as close to the choices a human would take or the most logical decision to make at the time. A quintessential example can be seen in AI cars, where data is obtained from sensors; maps; surveillance cameras, and traffic areas.

Speech to text and forecasting are two other uses and advantages.