Data Science with Python

Data Science with Python

Data Science with Python

Training Cost: $595.00
Training Type Instructor Based Online Training
Audience and Prerequisites Data Science and Artificial Intelligence

Who should take this course?

  • Big Data Specialists, Business Analysts and Business Intelligence professionals
  • Statisticians looking to improve their Big Data statistics skills
  • Developers wanting to learn Machine Learning (ML) Techniques
  • Information Architects looking to learn Predictive Analytics
  • Those looking to take up the roles of Data Scientist and Machine Learning Experts


This is a complete Data Science course that provides you detailed learning in data science, data analytics, project life cycle, data acquisition, analysis, statistical methods and machine learning. You will gain expertise to deploy Recommenders, data analysis, data transformation, experimentation and evaluation.

  • Introduction to Data Science in real world, Project Life cycle, and Data Acquisition
  • Understand Machine Learning Algorithms
  • Study the tools and techniques of Experimentation, Evaluation and Project Deployment
  • Learn the concept of Prediction and Analysis Segmentation through Clustering
  • Learn the basics of Big Data, Hadoop and Spark
  • Get trained about the roles and responsibilities of a Data Scientist
  • Live Projects on Data science, analytics and Recommender Systems
  • Work on data mining, data structures, data manipulation.


Curriculum: Complete Data Science with Python 

1. Introduction

  • Introduction to Data Science
  • Roles and Responsibility of a Data Scientist
  • Environment Setup and Installations
  • Jupytar Notebook

2. Python Crash Course

  • Basic Operations in Python
  • Variable Assignment
  • Functions: in-built functions, user defined functions
  • Condition: if, if-else, nested if-else, else-if
  • List: Different Data Types in a List, List in a List
  • Operations on a list: Slicing, Splicing, Sub-setting
  • Condition(true/false) on a List
  • Applying functions on a List
  • Dictionary: Index, Value
  • Operation on a Dictionary: Slicing, Splicing, Sub-setting
  • Condition(true/false) on a Dictionary
  • Applying functions on a Dictionary
  • Numpy Array: Data Types in an Array, Dimensions of an Array
  • Operations on Array: Slicing, Splicing, Sub-setting
  • Conditional(T/F) on an Array
  • Loops: For, While
  • Shorthand for For
  • Conditions in shorthand for For

3. Statistical Fundamenatals

  • Statistics & Plotting
  • Seabourn & Matplotlib - Introduction
  • Univariate Analysis on a Data
  • Plot the Data - Histogram plot
  • Find the distribution
  • Find mean, median and mode of the Data
  • Multiple data with different distributions
  • Bootstrapping and sub-setting
  • Making samples from the Data
  • Making stratified samples - covered in bivariate analysis
  • Find the mean of sample
  • Central limit theorem
  • Plotting
  • Hypothesis testing + DOE
  • Bivariate analysis
  • Correlation
  • Scatter plots
  • Making stratified samples
  • Categorical variables
  • Class variable

4. Introduction to Numpy

  • Numpy Arrays
  • Array Indexing
  • Numpy Array Indexing
  • Numpy Operations

5. Data Analysis with Pandas

  • File I/O
  • Series: Data Types in series, Index
  • Data Frame
  • Series to Data Frame
  • Re-indexing
  • Operations on Data Frame: Slicing, Splicing, Sub-setting
  • Pandas
  • Stat operations on Data Frame
  • Reading from different sources
  • Missing data treatment
  • Merge, join
  • Options for look and feel of data frame
  • Writing to file
  • db operations

6. Data Manipulation & Visualization

  • Data Aggregation, Filtering and Transforming
  • Lamda Functions
  • Apply, Group-by
  • Map, Filter and Reduce
  • Visualization
  • Matplotlib, pyplot
  • Seaborn
  • Scatter plot, histogram, density, heat-map, bar charts

7. Data Visualization with Python

  • Seaborn
  • Distribution Plot
  • Categorical Plot
  • Matrix Plot
  • regression Plot
  • Grid
  • Style and Color
  • Panada Data visualization
  • Geographical Plotting
  • Choropleth Maps

Introduction to Machine Learning

8. Linear Regression

  • Regression - Introduction
  • Linear Regression: Lasso, Ridge
  • Variable Selection
  • Forward & Backward Regression

9. Logistic Regression

  • Logistic Regression: Lasso, Ridge
  • Naive Bayes

10. Unsupervised Learning

  • Unsupervised Learning - Introduction
  • Distance Concepts
  • Classification
  • k nearest
  • Clustering
  • k means
  • Multidimensional Scaling
  • PCA

11. Random Forest:

  • Decision trees
  • Cart C4.5
  • Random Forest
  • Boosted Trees
  • Gradient Boosting

12. SVM:

  • SVM - Introduction
  • Hyper-plane
  • Hyper-plane to segregate to classes
  • Gamma

13. Big Data and Spark with Python

  • Big Data and Hadoop Overview
  • Spark Overview
  • Sprark Setup
  • Lambda Expression
  • RDD Transformation and Action

14. Neural Nats and deep Learning

  • Neural Network Theory
  • Deep Learning
  • Tensor Flow
  • tensor Flow Installation
  • MNIST with Multi-Layer Perceptron
  • TensonFlow with ContribLearn