Data Science Program

A comprehensive data science training course typically covers a wide range of topics to equip you with the skills and knowledge needed to work in the field of data science. Each topic is covered may vary depending on the course. Data science is an interdisciplinary field, so courses often include a mix of programming, statistics, machine learning, and domain-specific knowledge to prepare you for various roles in the industry.

  • Training from Industry Level Project Coders
  • Accredited Training Certification
  • Average Starting Package: 7-8 LPA
  • Top Hiring Partners

Batch Schedule for Data Science Program

Azsm Enterprizes provides flexible timings to all our students. Here are the Data Science Program Shedule for our branch. If this schedule doesn’t match please let us know. We will try to arrange appropriate timings based on your flexible timings.

Mon (Mon - Fri)Weekdays Batch 08:00 AM (IST)(Class 1Hr - 1:30Hrs) / Per Session Get Fee
Thu (Mon - Fri)Weekdays Batch 08:00 AM (IST)(Class 1Hr - 1:30Hrs) / Per Session Get Fee
Sat (Sat - Sun)Weekend Batch 11:00 AM (IST) (Class 3Hrs) / Per Session Get Fee

Why I Should Enroll for Data Science Program?

Data Science Program has been curated after thorough lookup and pointers from industry experts. It will help you differentiate yourself with multi-platform fluency, and have real-world experience with the most necessary equipment and platforms. Azsm will be via your side for the duration of the studying ride – We’re Ridiculously Committed.

  • Programming Python, Java, SQL, Scala, MTALAB & R Programming, JavaScript
  • Big Daata Platforms MonogoDB, MS Azure, & Oracle
  • Data Visualization Power BI, SAS, D3.js
  • Offered Fee Rs. 35,000/-
  • Discount Offered Fee Rs. 30,000/-
  • Amount To Pay (+GST) Rs. 30,000/-

Data Science Course Curriculum

Curriculum Designed by Experts

  • This software follows a set structure with 6 core courses and four electives spread throughout 21 weeks. It makes you a specialist in key technologies associated with Data Science Program. At the end of each core course, you will be working on a real-time venture to obtain palms-on expertise. By the quiet of the program, you will be ready for seasoned Data Science Program job roles.


    • Python

    • Flask

    • Numpy

    • Pandas

    • Visualization - QlikSense

    • Databases

    • EDA

    • Machine Learning

    • Deep learning

    • Natural language processing

    • Prompt Engineering

  • Introduction to Data Science:

    What is data science?

    • The data science workflow and life cycle.
    • The role of a data scientist.

    Data Collection and Storage:

    • Data sources and data types.
    • Data collection methods.
    • Data storage and databases.

    Data Cleaning and Preprocessing:

    • Data cleaning techniques.
    • Handling missing data.
    • Data normalization and scaling.

    Exploratory Data Analysis (EDA):

    • Data visualization.
    • Descriptive statistics.
    • Identifying patterns and trends in data.

    Data Analysis with Statistics:

    • Probability and statistics.
    • Hypothesis testing.
    • Statistical distributions.

    Machine Learning Fundamentals:

    • Supervised learning.
    • Unsupervised learning.
    • Model evaluation and validation.

    Linear Regression:

    • Simple and multiple linear regression.
    • Model evaluation and interpretation.
    • Classification Algorithms:

      • Logistic regression.
      • Decision trees.
      • Random forests.
      • Support vector machines.
      • K-Means clustering.
      • Hierarchical clustering.
      • DBSCAN.

      Feature Engineering:

      • Feature selection.
      • Feature extraction.
      • Handling categorical data.

    Clustering Algorithms:

    Time Series Analysis:

    • Time series data.
    • Forecasting techniques.

    Natural Language Processing (NLP):

    • Text preprocessing.
    • Text classification.
    • Named Entity Recognition (NER).

    Deep Learning:

    • Neural networks.
    • Convolutional Neural Networks (CNNs).
    • Recurrent Neural Networks (RNNs).

    Model Deployment:

    • Deploying models into production.
    • Model APIs and services.

    Big Data and Distributed Computing:

    • Hadoop and MapReduce.
    • Spark and distributed data processing.

    Data Visualization:

    • Data visualization libraries.
    • Creating interactive visualizations.

    Data Ethics and Privacy:

    • Ethical considerations in data science.
    • Data privacy and security.

    Capstone Project:

    • Applying data science skills to a real-world project.

    Tools and Technologies:

    • Popular data science tools and libraries (e.g., Python, R, scikit-learn, TensorFlow, pandas, etc.).

    Data Science in Industry:

    • Real-world applications of data science in various industries.