Data science is nothing less than a buzzword right now! It is undoubtedly one of the most desired professions for the new generation. Here, we are going to talk about:

  1. What is Data Science, and why do we even need it?
  2. What do Data Scientists do?
  3. How to Become a Data Scientist?


What Is Data Science, And Why Do We Even Need It?

In simple words, Data Science is the art and science of exploring data to find solutions to a particular problem. Multiple processes, formulas, and algorithms are used in order to extract meaningful insights from the data, which will eventually be used to make a decision.

Data science has various use cases such as Predicting sales, finding the solution to a problem like less user retention, or finding out how often people visit your store, etc. Data science has applications in almost every industry out there.


What Do Data Scientists Do?

Data scientists have various responsibilities to take care of. They work closely with high-table people to understand company goals and potential problems and discover how to do the needed with the help of data. They collect data concerning problems or objectives, create algorithms and multiple models to extract and analyze the data, which will eventually be shared with the high-table with the help of data visualization.


Roles And Responsibilities Of A Data Scientist

  1. Finding out the problem or the goal.
  2. Acquiring data aligning to the problem or the objective.
  3. Processing, cleaning, and storing the data
  4. Performing exploratory data analysis
  5. Choosing potential algorithms and models
  6. Applying various data science tools and techniques to get some results
  7. Measuring the results and also hand in hand improving the results if needed
  8. Presenting final result to stakeholders and making the adjustments based on feedback


How To Become A Data Scientist In Today’s Data-Driven World?

I would say becoming a data scientist is rather easy in today’s world as compared to the past. Because now we have better access to the internet as well as technology and resources, which makes the oath quite easy for everyone who wishes to pursue data science in current times.

Anyways, here I have divided the whole journey of becoming a data scientist into five simple steps as follows-


Step 1: Get Comfortable With Programming Languages

Having a good command of programming languages is a must if you want to become a data scientist, as most of your work will dwell around programming languages, especially Python and R.

Python and R both languages have an arsenal of packages and libraries that support the data science workflow.

Once you have mastered everything around these languages, it is time to move on to the next step.


Step 2: Learn Statistics

Data science is incomplete without statistics. Statistics is just like the grammar of data science used to analyze and interpret huge data sets. Important tasks in the data science process like data gathering and analysis are all done with the help of statistics. So, it is crucial to have a deeper understanding of statistics if you are on the path of becoming a data scientist.


Step 3: Master Data Cleaning and EDA (Exploratory Data Analysis)

Did you know that majority of a data scientist’s time is spent on data cleansing? Well yeah! It is really that important and time-consuming.

Data cleaning basically removes unwanted values, outliers, improperly submitted records and fills the missing values from the Raw form of data. Real-world data is messy and can cause problems if not appropriately cleansed in the initial stage.

EDA is yet another critical aspect in data science, which includes analyzing various variables, data patterns, and trends to extract valuable information with the help of different graphical and statistical methods.

Having a good command of both of these aspects is a must; otherwise, you won’t be able to become a data scientist no matter what you do.


Step 4: Machine Learning & Deep Learning

In Data Science, Machine learning is used to make different classification models and predictive models to predict future outcomes and possibilities (based on data) and to make a plan accordingly. In comparison, Deep Learning is an advanced version of Machine Learning which uses Neural Networks that combine multiple machine learning algorithms to solve complicated tasks much faster.

Both machine learning and deep learning are undoubtedly the most crucial skills that set you apart from your peers.


Step 5: Master The Soft Skills

While technical skills will help you work, soft skills will help you grow in your career and get to the top positions. Soft skills include Networking, Communication Skills, Task management, Business understanding, etc.



These were all the steps that you need to follow to become a top-notch data scientist. Moreover, you should also keep participating in different workshops, community events, and forums to expand your knowledge and network. And stay updated on all the trends and keep upskilling yourself.