Throw more light on Data Analytics, Data Science and Machine Learning ,

Everything Else Career Advice
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Hello, I’m new to Python hoping to hone my skills in Data Science, Can someone help to throw more light on Data Science, Data Analytics and Machine Learning the different and job roles, :clap:t6::clap:t6::clap:t5: Thanks!.,

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Here is Coursera’s take on the different roles (see link below). I have to add that, in my experience (30 years in data roles) that in many companies/shops, there is significant overlap in data roles. Most of the articles I’ve read lately do consider Data Analyst (closer to the data itself) and Data Scientist (a more global, enterprise-wide view) to be different, with Data Science considered as more senior.

I started out in Computer Operations, and moved up to Database Administration (DBA). The DBA role at that time covered database administration, data analytics and modeling, and the enterprise data environment (i.e., data science). Today, the data world resembles other professional fields; where once a professional could “know it all”, now “specialists” are the norm.

Link for reference only; not an endorsement.

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Thanks Sir for this infor!… :clap:

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Great question — and it’s smart that you’re starting with Python, since it’s the backbone for all three fields. Let’s break it down:

:small_blue_diamond: Data Analytics

  • Focus: Analyzing historical data to find patterns, trends, and insights.

  • Tools/Skills: SQL, Excel, Power BI/Tableau, Python (Pandas, NumPy, Matplotlib).

  • Job Role: Data Analyst — preparing dashboards, doing Exploratory Data Analysis (EDA), answering “what happened” and “why.”

:small_blue_diamond: Data Science

  • Focus: A broader field that includes analytics + predictive modeling + statistics.

  • Tools/Skills: Python, R, SQL, statistics, ML libraries (Scikit-learn, TensorFlow, PyTorch).

  • Job Role: Data Scientist — uses advanced ML models to predict “what will happen” and “what should we do.”

:small_blue_diamond: Machine Learning (ML)

  • Focus: A subset of Data Science that uses algorithms to “learn” from data and make predictions.

  • Tools/Skills: Python, ML frameworks, cloud deployment.

  • Job Role: ML Engineer — builds recommendation systems, fraud detection models, NLP chatbots, etc.

:white_check_mark: How to Build a Career Path

  1. Start with Data Analytics → learn SQL, Python basics, and dashboards.

  2. Move into Data Science → add ML, stats, and real-world projects.

  3. Specialize in ML/AI → deployment, NLP, deep learning, etc.

:pushpin: For Freshers Looking for Internships

If you’re just starting out, a structured internship can give you practical exposure. Programs like the 360DigiTMG Data Analyst Internship for Freshers cover Python, SQL, EDA, dashboards, and business case studies, making it easier to move into Data Science later.

Pro tip: Build at least 3–5 projects (e.g., sales dashboard in Power BI, EDA on large datasets, a simple ML model). Share them on GitHub + LinkedIn — recruiters value projects more than certificates alone.