Data Science With Machine Learning

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Data Science With Machine Learning

The Certificate Program on Data Science is one of the most comprehensive Data Science Courses in India. it is specially designed to suit both data professionals and beginners who want to make a career in this fast-growing profession. Over 3 months, students will learn key techniques such as Statistical Analysis, Regression Analysis, Data Mining, Machine Learning, Forecasting and Text Mining, and tools such as Phython and R Programming.

Data Science Curriculum-45 days

  1. Introduction to Data Science.
  2. Introduction to technologies relating to data science.
  3. Introduction to CRISP-DM
  4. Module 1:From Problem to approach.
  5. Module 1:From Problem to approach.
  6. Module 2:From Requirements to Collection.
  7. Module 3:From Understanding to Preparation.
  8. Module 4:From Modeling to Evaluation.
  9. Module 5:From Deployment to Feedback.
  10. Sample Assignment.
  1. Introduction to statistics.
  2. Certain terminologies related to Statistics.
  3. Introduction to probability.
  4. Introduction to Inferential Statistics.
  5. Hypothesis Testing.
  6. Evaluation metric techniques.
  1. Basics-types,Expressions,Variables,String Operations.
  2. Python Data Structures.
  3. Programming Fundamentals.
  4. Working with data in python.
  5. Final Project.
  1. Introduction to Databases.
  2. Basic SQL.
  3. String Patterns,Ranges,Sorting and Grouping.
  4. Functions,Sub-Queries,Multiple Tables.
  5. Accessing Databases using Python.
  6. Using JOIN operators to work with Multiple Tables.
  7. Project.
  1. Importing Datasets- Understanding the data.
  2. Data Wrangling / Normalisation.
  3. Exploratory Data Analysis.
  4. Model Development.
  5. Model Evaluation and Refinement.
  6. Project.
  1. Introduction to Data Visualisation.
  2. Introduction to Matplotlib.
  3. Basic Visualisation Tools.
  4. Advanced Visualisation and Geospatial Data.
  5. Sample Project.
  1. Introduction to Machine Learning.
  2. Supervised vs Unsupervised Learning.
  3. Machine Learning vs Deep Learning vs Data Science vs AI.
  4. Linear Regression.
  5. Non-Linear Regression.
  6. Polynomial Regression.
  7. Introduction to Classification.
  8. Introduction to Clustering.
  9. Content-based Recommendation Systems.
  10. Project based on Linear Regression, Polynomial , Multiple Linear Regression.
  11. Project based on Classification Models.
  12. Project based on Clustering.
  13. FourSquare API.
  14. Project Introduction.
  15. Neighbourhood Segmentation and clustering.
  16. Final Project.

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