What is a Data Scientist?

A data scientist uses data to understand and explain the phenomena around them and helps organizations make better decisions. They are part mathematician, part computer scientist, and part trend-spotter. Straddling both the business and IT worlds, they are highly sought-after and well-paid. Who wouldn’t want to be one?

What Does a Data Scientist Do?

A data scientist’s primary responsibility is to extract meaningful information from data and analyze it. Typical job duties and responsibilities of a data scientist include:

• Designing data modeling strategies and suggesting economic modifications to existing methods and strategies.
• Building machine learning algorithms and data models to forecast outcomes.
• Continuously extracting massive volumes of structured data (using programming languages like SQL) as well as unstructured data (via APIs, web scraping, and surveys). Collaborating with various departments to solve business concerns via scientific tools.
• Cleaning data to remove irrelevant information and conducting exploratory data analysis to address missing data.

Data Analyst vs Data Scientist: What’s the Difference?

The work of data analysts and data scientists can seem similar—both find trends or patterns in data to help organizations make better decisions about operations. However, data scientists tend to have more responsibility and are generally considered more senior than data analysts.

• Data Analysts: Support teams with set goals, understanding business problems, identifying relevant datasets, cleaning, blending, and analyzing data to help businesses make informed decisions.
• Data Scientists: Form their own questions about data, develop models using machine learning, and incorporate advanced programming to find and analyze data. Their responsibilities include developing and implementing predictive models and algorithms to solve strategic business problems.

Why Become a Data Scientist in India?

• Average Salary: ₹956K/Yr (Source: Glassdoor)
• Demand Increase: 62% expected increase (Source: TOI)
• Opportunities: Internet growth leads to more data, creating opportunities in data science.
• Top Companies: Google, Microsoft, Flipkart, and Amazon are constantly searching for talented data scientists.

A Roadmap to Becoming a Data Scientist

There hasn't been a better time to get into data science and build your career. Here's a step-by-step roadmap to becoming a data scientist in 2023:

1. Mathematics & Statistics

Understanding mathematics and statistics is fundamental before diving into programming.

• Learn basic statistics from resources like Hines (PDF available online).
• Understand concepts like dy/dx, optimization, and gradient descent. Use playlists and tutorials to grasp these basics.
• Plot simple functions in Excel to practice.
• Focus on probability distributions, especially normal distribution.

2. Programming

Python is recommended due to its popularity and widespread use in data science.

• Learn Python basics.
• Explore libraries like NumPy (CodeWithHarry has great videos).
• Learn Pandas for data manipulation.
• Use Matplotlib/Seaborn for data visualization.
• Understand time complexity of algorithms and database storage.

3. Big Data and External Data Visualization Tools (Optional)

While Python can handle data visualization and big data, external tools can be more efficient for specific tasks.

• Learn tools like Tableau, Excel (& VBA), Hadoop, and AWS offerings.
• Note that mastering these tools might take time.

4. Machine Learning and Deep Learning

After mastering data wrangling with Python, focus on machine learning concepts.

• Start with Google's free machine learning course.
• Learn Scikit-Learn (Sklearn).
• Build neural networks with TensorFlow.
• Explore TensorFlow Hub and TensorBoard.
• Consider free courses from Kaggle and Coursera.

5. Linux & Version Control

Managing and collaborating on software projects is essential.

• Learn Linux basics.
• Master Git for version control.
• Follow papers with code, GitHub trending repositories, and data science news.
• Learn to scrape data from websites.

Other Points to Remember

• Computer Science Degree: Helpful but not essential.
• Domain-Specific Knowledge: Increases job prospects.
• Communication Skills: Good communication and spoken English are valuable.
• Staying Competitive: Staying ahead of the competition is crucial.

I wish you all the best in learning data science. I hope this information helps you on your journey to becoming a data scientist.