Harbola DataScience

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Why IT professionals are Transitioning to Data Science?

To make an impression, and to boost your career growth in data science, you need to learn and have work-ready experience in a variety of programming languages, such as R, Python, have knowledge of machine learning algorithms, deep learning neural networks, nlp, computer vision techniques. Learning test-driven Python development and understanding SQL (Structured Query Language) is a must.

Data Science Career is the hottest and most demanded topic in the market among the youth in 2022. Data Science uses numerous statistical approaches. These approaches range from data transformations, data modeling, statistical operations (descriptive and inferential statistics), and machine learning modeling.

A data scientist is a computer professional possessing skills for collecting, analyzing, processing a large set of structured and unstructured data. In this time of computers, most organizations are collecting a huge amount of data in their daily operations.

Here is why you should not focus on getting certification rather getting skilled because Generally, a “Data Science Certificate” on a resume will not demonstrate what skills that certification represents. This is why employers tend not to give them much weight when qualifying candidates.

Age is not a barrier to start a career in data science. Some started their data science career in 45. I started at 16 😉

So is data science still a rising career in 2021?

The answer is a resounding YES! Demand across the world for Data Scientists are in no way of slowing down, and the lack of competition for these jobs makes data science a very lucrative option for a career path.

Yes, data science is a very good career with tremendous opportunities for advancement in the future. Already, demand is high, salaries are competitive, and the perks are numerous – which is why Data Scientist has been called “the most promising career” LinkedIn and the “best job in America” Glassdoor.

Altogether, the amount of learning that is required to become a data scientist cannot be done in a mere time period of six months

You can think about the data increase from IoT or from social data at the edge. If we look a little bit more ahead, the US Bureau of Labor Statistics predicts that  2026—so around six years from now—there will be 11.5 million jobs in data science and analytics.

Coding is required for data science.

Data science requires the use of coding languages to explore, clean, analyze and present data. Coding languages like Python and R are also used in machine learning in data science.

Data science is a rapidly growing industry, and advances in technology will continue to increase demand for this specialized skill. While data science does involve coding, it does not require extensive knowledge of software engineering or advanced programming.

Job Opportunities in Data Science

According to the U.S. Bureau of Labor Statistics (2021), the data science and computer information research field is expected to grow 22% from 2020–2030 which is triple the rate of the average profession.

Some people compare career paths like data science vs programming because both require analysis and programming experience. But data science careers have a far greater emphasis on analytical elements, while programming has a far greater emphasis on developing proficiency working with multiple programming languages.

In India, the average entry-level data scientist income is 511,468 rupees per annum for a recent graduate. A data scientist in their early career with 1-4 years of experience earns an average of Rs. 773,442 per year.

Do you need Python for data science?

Python is a general purpose language, used data scientists and developers, which makes it easy to collaborate across your organization through its simple syntax. People choose to use Python so that they can communicate with other people. The other reason is rooted in academic research and statistical models.

It provides great libraries to deals with data science application. One of the main reasons why Python is widely used in the scientific and research communities is because of its ease of use and simple syntax which makes it easy to adapt for people who do not have an engineering background.

R programming language is harder than Python. R can be difficult for beginners to learn due to its non-standardized code. Python is usually easier for most learners and has a smoother linear curve. In addition, Python requires less coding time since it’s easier to maintain and has a syntax similar to the English language.

It can take anywhere from four to 7 weeks to learn Python programming for data analysis, although this depends on how much experience you have with programming languages and web development. Generally speaking, though, Python can be considered very beginner-friendly, as it is known for its readability and ease of use.

Is Python better than Excel?Excel is powerful, but Python will upgrade your data science and analytics workflow because you can integrate data extraction, wrangling, and analytics in one environment. Most importantly, you can show all your work in containers that will make it easier to fix mistakes than Excel.

Python is the most popular language among data analysts and data scientists due to its extensive set of graphical options and visualization tools that make data more available.

Tableau allows for more interactivity and is easier to make plots with than coding. The most important when deciding which one to use is regarding the workflow. Python is the best when working with a variety of data that requires advanced analytics.


How long does it take to transition to data science?

You can learn the skills needed to become a Data Scientist in as little as 12 weeks, which is why it has become increasingly common for neophyte Data Scientists to attend data science bootcamps, which allow for more hands-on learning and targeted skills development.

How do I become a data scientist with no experience? No Experience? Here is How To Get Your First Data Science Job

  1. Technical skills. building your technical skills from ground zero
  2. Building a portfolio.
  3. Writing about your work. Share your learnings and experience.
  4. Creating an impressive resume.
  5. Networking and having a mentor. One of the most important.
  6. Go for growing companies.
  7. DO NOT hesitate to take up data roles.

Needless to say, a data scientist must be familiar with the important concepts of math, statistics, and probability, know either Python, R, or SQL, have knowledge of one or more data visualization tools and possess other soft skills like business acumen, communication skills, and storytelling skills.

How to kick start your data science career

  • Step 0: Figure out what you need to learn.
  • Step 1: Get comfortable with Python.
  • Step 2: Learn data analysis, manipulation, and visualization with pandas.
  • Step 3: Familiarize Yourself with Tools and Frameworks.
  • Step 4: Learn machine learning with scikit-learn.
  • Step 5: Find a Community.
  • Step 6: Understand machine learning in more depth.

these were the 6 Steps to Learn Data Science From Scratch.

Google about machine learning, deep learning and search for doubts you have.

Technical Skills Required to Become a Data Scientist

  • Programming.
  • Statistical analysis and computing.
  • Machine Learning.
  • Deep Learning.
  • Processing large data sets.
  • Data Visualization.
  • Data Wrangling.
  • Mathematics.

This is a blog that explain in detail about the technical skills for data science.

7 Steps to a Successful Data Science Project

  1. Problem Statement.
  2. Data Collection.
  3. Data Cleaning.
  4. Exploratory Data Analysis (EDA)
  5. Feature Engineering.
  6. Modelling.
  7. Communication.

Follow this guide to know in detail.

Steps to your First Data Science Project

  1. Choose a dataset. If you are taking up the data science project for the first time, choose a dataset of your interest. .
  2. Choose an IDE.
  3. List down the activities clearly.
  4. Take up the tasks one one.
  5. Prepare a summary.
  6. Share it on open source platforms.

What is data science project life cycle?

In simple terms, a data science life cycle is nothing but a repetitive set of steps that you need to take to complete and deliver a project/product to your client.

How do you list data science skills on a resume?

Data science resumes should include technical skills that are relevant to the position you are applying for. A good strategy is to first list all your data science skills, including any software and tools. Next, review the job description and highlight the skills that are required in the role.

Chitranshu Harbola

Self taught programmer, Web Developer and an aspiring Machine learning engineer cum Data Science student

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