A complete Data Science Beginners Guide is all you need to build your career in Data Science. 

Data Science for beginners can be a bit confusing but we have you covered.

This might be the first time that you chanced upon the field, there will be a bunch of questions hovering your mind.

– What exactly is Data Science? 
– What all things are included in this? 
– Is it relevant to the current business scenario? 
– Will it fetch a lucrative job? 
– How does Data Science work? 
– Is it an exaggeration or worth it? 
– What is its future?

We are sure you have many more doubts and we will answer them all for you in our Data Science Beginners Guide. 

What is Data Science?

Clearing the Basics

You recognize the above image surely!

These are the search results of the search term “Google Assistant”. Do you know what does the highlighted number indicate?

This is the total number of search results that your search term yielded. But how was this possible? How did Google know all this? This is possible only with the help of the organized way of using data, i.e., Data Science.

Internet Search is one of the basic examples of the use of Data Science and there are many others like predicting customer behavior, fraud detection, etc.

Data Science is an interdisciplinary field that uses data to generate insights and put it to use for the benefit of the business. It is the process to extract, prepare, analyze, visualize and maintain information.

According to a report, more than 2.5 quintillion bytes of data are generated every day and by 2020, we will generate 1.7MB of data per second.

These numbers are per person; just imagine how massive the collective numbers would be? What’s the best part? You have a chance to examine the data and generate meaningful insights from it. 

Unlike the prevalent myth, Data Science is not just science but a mix of science and art. Along with the scientific aptitude, you have to have a great combination of logical, analytical and reasoning skills.

Why?

Data Science isn’t just a specific skill but a practice on a whole.

Why is Data Science Important?

Now that you know what Data Science is, you need to understand just how important the field has become to businesses!

Every organization intends to mine data to derive useful insights to grow their businesses. Data Science professionals work on huge datasets that are both structured and unstructured. 

Whether it is recognizing the market trends, analyzing consumer behavior or doing a competitor analysis, Data Science is extremely helpful for businesses to derive meaning from a huge pile of numbers. What’s even amazing about Data Science is that its application is not only in the IT industry but a variety of other industries, such as healthcare, e-commerce, entertainment, retail, etc. 

Does it seem a little overwhelming? This is why you need our Data Science Beginners Guide!

Data Science For Beginners

Here is an overview of Data Science for beginners: 

Feature	Data Science
Focus Area	Processing large datasets to generate insights and solve business problems to enable better decisions
Characteristic	Makes use of Big Data to analyze the data. Skills required in different fields like statistics, visualization and machine learning are much needed here.
Fundamental Problem Statement	– Classifying data – Processing data – Deriving insights – Making decisions
Significance	– Customer segregation – User-oriented product development – Make relevant and useful predictions
Career Opportunities	– Data Scientist – Data Engineer – Data Analyst

How to get a job in Data Science in 2019?

We’re sure you must’ve heard that Data Scientist is the Sexiest Job of the 21st Century!

But it doesn’t mean that there is just one profile in the realm of Data Science jobs. There are several other related roles in the field of Data Science.

Before you explore the different designations and profiles in our Data Science Beginners Guide, you need to follow the job market and understand how a sudden boost in Data Science has paved the way for huge job opportunities.

Data Science for beginners is all about understanding that irrespective of the job role you are aiming at, you need to master three core skills. 

You will be better off if you have a combined knowledge of mathematics, computer science, and domain expertise. Now, there are many people like you who wish to make a mark in this field but before you do so, it is a wise step to know the first steps to take. This will be to know the trends, preferences of the employers and other important things.

When you start with your job search, know which position to target, based on your experience. Our Data Science Beginners Guide also discusses different job roles.

Scope of Jobs in Data Science

No Data Science Beginners Guide would be complete without discussing the job aspect. Each Data Science role has their own roles and job specifications. Mind you, Data Scientist isn’t the only person in a Data Science project team. But yes, this is a desired position.

The demand for Data Scientists is growing at an unprecedented rate and this has increased by 650% since 2012. 

These figures to not just allude to seasoned and experienced Data Scientists. There are plenty of job opportunities in Data Science for beginners.

Do you know who is a Data Scientist and what he does? Did you say analyze the trends? You are confused between a Data Analyst and a Data Scientist. Both of these roles are different. To start with, here is a brief about one of the most sought-after and important roles in the Data Science job domain.

Who is a Data Scientist?

We will start our Data Science Beginners Guide by answering this simple question.

Data Scientists are not just the coding experts but also master storytellers, who have answers to all the queries of the non-technical stakeholders. They use a variety of tools, languages, and processes to work on data and derive insights. 

Data Scientists help businesses to make informed decisions that benefit them in the long run. Data Scientists are the experts in machine learning and statistics who have moved up the ladder from being a Data Analyst.

These Unicorns (rare skills; aren’t easy to spot) can do what Data/Business Analyst does and even more than that; Data Scientists constantly work on building and increasing the efficiency of the machine learning models.

Popular Data Science Tools

If you are still reading this article, then it means you are serious about increasing your Data Science knowledge base. So, now you are a pretty informed person; you know what is Data Science, why is it important, the job aspect of it and one of the most desired job roles- Data Scientists. 

What next? There are some popular tools of Data Science for beginners using which these professionals transform the numbers into meaningful data.

Here goes a list of a few of these crucial tools, in brief. 

1. Python

Our Data Science Beginners Guide will be incomplete without mentioning Python. One of the most popular tools in the Data Science realm, Python is an easy to learn tool/language that houses vast libraries to support data manipulation.

2. SAS

SAS offers several statistical tools and libraries for data organization and data modeling. It has a GUI interface, which makes it an easy tool to learn and work on.

3. Tableau

A most loved tool for Data Visualization, Tableau lets you create and customize your reports with easy to use tools and functionalities. There is a catch here. This tool can also be used for data analytics.

4. MATLAB

Next on our list in the Data Science Beginners Guide is MATLAB. MATLAB is a revered tool for use in scientific disciplines. This Data Science tool lets you work on statistical data modeling, matrix functions and implementing algorithms.

5. Sci-kit Learn

Sci-kit learn is a Python-enabled library through which it is easy to apply machine learning algorithms. This is the favorite tool of Data Science for beginners to make Machine Learning research and rapid prototyping easier.

How to make your numbers speak?

Numbers become data when they are worked upon by the Data Science professionals. Do you know how is it done? What are the processes?

Have a look!

*The tools shown in the above processes are not the only ones used; there are many other ways/tools to carry out the above procedures. There might be 1 or even 2 tools to carry out these processes. It depends on the businesses and organizations.

Now that you know the processes, here is a brief overview of the process which the Data Scientists follow to make the numbers speak. 

Needless to say that the objective of Data Science is to make use of huge data available and provide a solution to real-world problems. The process starts with the cleaning of dataset and arranging it in a pre-defined format. This involves eliminating several inconsistencies and errors from the unstructured dataset and making it a structured dataset. 

After this, several statistical procedures are used to analyze the data. Out of these, two procedures are:

1. Descriptive Statistics
2. Inferential Statistics

Take the example of App-based cab service here. 

Ola has employed you as a Data Scientist to analyze the total number of customers. It may look easy but that’s not the case.

There are several factors that need to be kept in mind; there might be some people who used cab just for a single booking and then for some reason deleted the app, while others are loyal customers using it for years and so on.

Once you have the required data, you present it to the stakeholders in a graphical, visually appealing format. Did you realize what you did just now? You just applied descriptive statistics to solve the problem statement.

Now inferential statistics comes into the picture.

After you have the data, you are required to draw conclusions/inferences from it.

Picture this- With respect to the current cab market in India, you now need to study the effect of various factors like ease of availability, rates, technology trends, types of payment options etc.

These variables will help you derive an inference about consumer preference. As it is not feasible to go to each customer and get the answers, you create a questionnaire that is sent through mail to a few customers.

Thereafter, you record the responses and draw an inference that due to the ease of availability and different customer-centered factors like GPS tracking, ease of payment etc. app-based cab service is a successful business.

The project now requires you to predict the number of customers in 5 years from now and thus the increase in revenue. Here you will use Regression Algorithms. On the basis of past sales figures, regression algorithm will help you to make this prediction. 

Post this, there are many factors that can be analyzed, such as the peak booking hours, the areas of maximum and minimum demands, the type of rides booked- local or outstation, ratings given by customers etc. 

Now that you have a broad understanding of the tools and skills required to become a Data Scientist, the next part of our Data Science Beginners Guide deals with the applications of Data Science.

Data Science Applications

With continuous development in all the fields, there are lots of new applications that are developed to make our lives easy. These applications can be seen in almost every sector from e-commerce and healthcare to transport and banking etc.

The Future of Data Science

Data Science is here to stay. It will increase in size with the increase in amount of data generated every second.

This is the actual future of Data Science.

As per the U.S. Bureau of Labor Statistics, around 11.5 million jobs will be created by 2026. According to LinkedIn, one of the most budding jobs in 2019 is that of a Data Scientist. Data Scientist topped LinkedIn’s list of the Most Promising Jobs of 2019. This booming field held the 9th slot in the last year i.e., 2018. 

Data science is going to become even more consumer-friendly and helpful to us in the future, automating processes and creating smarter systems.

The process that starts with data collection then moves a step further to making machines learn, finally ends with making machines intelligent and minimize human intervention. 

This ends our Data Science Beginners Guide 2019! We hope you now have a structured understanding of how to go about approaching your Data Science aspirations. If you have any questions, please write to us in the comments section below.