Objective

SK-CDA with Python is the second level in the Data Scientist Track. This course is specifically designed for aspiring Data Analysts, Data Scientists, ML Engineers and Deep Learning Engineers. The Curriculum is focused to build a strong analytics foundation for aspiring Data Scientists.

Why SK-CDA

  • Good exposure of libraries used in data science
  • Learn about several data wrangling functions
  • Statistical concepts for Data Science
  • Learn exploratory data analysis techniques

Pre requisites

  • Python Programming Knowledge or CPP Certification


Module 01: Introduction to Numpy

  • Creating an Array
  • Reading text files
  • Indexing of Arrays
  • Datatypes
  • Array operations
    1. Mathematical Operations
    2. Methods
    3. Reshaping & combining arrays
    4. Comparison & Filtering

Module 02: Data Wrangling with Pandas

  • Import libraries, read files with different file formats
  • Descriptive statistics
  • Lambda functions to slice & dice data
  • Index operations
  • Utility functions
  • Identify outliers
  • Data Munging
  • Aggregation
  • Reshaping the data

Module 03: Visualization with Matplotlib

  • Simple plot
  • Figures, Subplots, Axes and Ticks
  • Animation
  • Other Types of Plots
  • Pyplot
  • Working with Numpy arrays as images
  • Working with Text
  • Customizing your products
  • Transformations

Module 04: Data Visualization Using Matplotlib

  • Sets & Counting
  • Combinatorics
  • Probability
  • Random Variables
  • Probability Distributions
  • Central limit theorem
  • Statistics, Parameter Estimation & Confidence Interval
  • PCA
  • Hypothesis Testing

Module 05: Project

Capstone Project



40 hours of instructor led training

Contact Us

+91 93 848408 00

Request more information

Module 01: Introduction to Numpy

  • Creating an Array
  • Reading text files
  • Indexing of Arrays
  • Datatypes
  • Array operations
    1. Mathematical Operations
    2. Methods
    3. Reshaping & combining arrays
    4. Comparison & Filtering

Module 02: Data Wrangling with Pandas

  • Import libraries, read files with different file formats
  • Descriptive statistics
  • Lambda functions to slice & dice data
  • Index operations
  • Utility functions
  • Identify outliers
  • Data Munging
  • Aggregation
  • Reshaping the data

Module 03: Visualization with Matplotlib

  • Simple plot
  • Figures, Subplots, Axes and Ticks
  • Animation
  • Other Types of Plots
  • Pyplot
  • Working with Numpy arrays as images
  • Working with Text
  • Customizing your products
  • Transformations

Module 04: Probability & Statistics with Python

  • Sets & Counting
  • Combinatorics
  • Probability
  • Random Variables
  • Probability Distributions
  • Central limit theorem
  • Statistics, Parameter Estimation & Confidence Interval
  • PCA
  • Hypothesis Testing

Module 05: Project

Capstone Project

40 hours of instructor led training

Training Schedules

29th September 2018

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Course Curriculum

Introduction to Software Training Details FREE 00:40:00
Object Oriented Design Patterns Details 00:35:00
Software Testing Details 00:30:00
Advanced Database Development Details 00:25:00
Algorithm analysis Details 00:45:00
Multi Threading in Softwares Details 00:40:00
Managing Software Testing Details 00:20:00
The Software Quiz 00:04:00

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