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Data Science Using Python डेटा साइंस यूज़िंग पायथन

Ratings (4.5)
60 Hours
Live Classes
60 Videos
Training by Instructor
80 Labs
Hands-on Labs
Real-World Project
Learn to Implement
Digital Notes
Doubt Clear Session

About the Course

This course covers Python programming, data analysis, machine learning, and data visualization. Students will gain essential knowledge in these areas and master the tools of Data Science with Python.

Benefits of learning Data Science Using Python

Through this training, student will gain knowledge in data analysis, Machine Learning and data visualization.

  1. After completing the module, the learner will be able to:
  2. Introduction to Python Programming and statements.
  3. Understand and master the concept of Modular Programming, using Function, Module and Packages.
  4. Understand and master the concept of Sequence Data Types – List, Set, Dictionaries.
  5. Understand and master the concept of NumPy and Matplotlib.
  6. Understand the concept of Statistical functions.
  7. Understand the concepts of Data Science and Analysis tools- Pandas
  8. Understand the concept of Machine Learning
      9. Supervised
    1. Regression Algorithms
    2. Classification Algorithms
      Unsupervised
    1. Clustering Algorithms
Job Roles of Course

This Course is designed to equip a learner with necessary skills as per following job roles:

  1. Junior Data Analyst
  2. Python Programmer
  3. Python Associate

Syllabus

Module 1: Python
  1. Python Introduction, Program execution, Using Comments, Literals, Constants, Python’s Built-in Data types, Numbers (Integers, Floats, Complex Numbers, Real, Sets), Strings(Slicing, Indexing, Concatenation, other operations on Strings), Accepting input from Console, printing statements
  2. Assignment statement, expressions, Conditional statements: if, ifelse, if-elif-else; While Statement, For loop, break statement, Continue Statement, Pass statement.
  3. Lists, tuples and dictionary, (Slicing, Indexing, Concatenation, other operations on Sequence datatype), concept of mutability, Examples of finding the maximum, minimum, mean; linear search on list/tuple of numbers, and counting the frequency of elements in a list using adictionary.
  4. Basic Functions, Module Basics, Module Files as Namespaces, Import Model, Reloading Modules.
Module 2: Data Science
  1. NumPy Library, Numpy functions, Numpy Indexing, Broadcasting, Storage of images in Numpy, reading csv files in Numpy, Anal
  2. Pandas Library, working with dataframes and Series, loading csv, manipulating dataframes, Applying custom functions on dataframes, Aggregation functions, Filtering Data, Analysis.
  3. Visualization using matplotlib and Seaborn.
Module 3: Machine Learning
  1. Data Preparation, Feature Selection and Engineering, Training Data, Testing Data, Scikit Learn library, Predictive Machine Learning Algorithms - Regression , classification & clustering,
  2. Data Science Concepts
  3. Framing the problem, collecting , processing, cleaning and munging the data
  4. Data analysis and Visualizing results
  5. Machine Learning Basics and its Types
  6. Implementation of Regression, clustering and classification algorithms on data.

Project : 03 Project based on real world case study.

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