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Course Overview

 

This course helps the candidate in gaining expertise in Quantitative Analysis, data mining, and the presentation of data to see beyond the numbers thus preparing the candidate for a Data Scientist role. The candidate will use libraries like Pandas, NumPy, Matplotlib, Scikit and master the concepts like Python Machine Learning Algorithms such as Regression, Clustering, Decision Trees, Random Forest, Naïve Bayes. Throughout the Course, the candidate will have the opportunity to solve real-life case studies.

 

Learning Outcome

 

On successful completion of the course, a candidate will be able to:

 

1. Understand characteristics of a dataset and do exploratory data analysis.

2. Decide on an appropriate machine learning algorithm to solve a business problem.

3. Train and build a predictive model using Python for aiding business decisions.

4. Test accuracy of model and do predictions on future data using the predictive model.

 

Course Duration

36 Hours approx..

 

 

                   Course Curriculum

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Topics

 

 

1. Introduction to Data Analytics, Machine Learning, AI and Python.

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• Introduction to Data Analytics, Machine Learning and Artificial Intelligence.

• Factors driving surge in popularity of Data Analytics and Machine Learning.

• Data Analytics Use Cases from multiple industries / sectors.

• Data Ecosystem of an Organisation.

• Difference between Business Analytics and Data Analytics.

• Skill set for Data Analytics professional.

• Languages / Tools used for Data Analytics.

• Python in comparison to other analytics tools.

• Introduction to Ecosystem and Community for self-support post course completion.

 

2.  Installation of Anaconda / Python and Jupyter Notebook / Spyder.

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• Installation of Anaconda / Python

• Installation of Jupyter Notebook / Spyder

• Basic operations of Jupyter Notebook / Spyder.

 

3.  Python programming – Basics 1

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• Values, Types, Variables,

• Operands and Expressions

• Strings and related operations

• Tuples and related operations

• Lists and related operations

•  Dictionaries and related operations

• Sets and related operations

• Conditional Statements

• Loops

• Regular Expression

• Command Line Arguments

• Writing to the screen

 

4.  Python programming – Basics II

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• Functions - Syntax, Arguments, Keyword, Arguments, Return Values

• Lambda - Features, Syntax, Options, Compared with the Functions.

• Sorting - Sequences, Dictionaries, Limitations of Sorting

• Errors and Exceptions - Types of Issues, Remediation

• Packages and Module - Modules, Import Options, sys Path

 

5.  NumPy and Pandas

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• NumPy – arrays

• Operations on arrays

• Indexing slicing and iterating

• Reading and writing arrays on files

• Pandas - data structures & index

• Operations

• Reading and Writing data from Excel/CSV

• formats into Pandas

 

6.  Data Visualisation and Exploratory Data Analysis.

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• Co-relation matrix

• Heatmap

• Introduction to Matplotlib

• Introduction to Seaborn

• Grids, axes, plots

• Markers, colours, fonts and styling

• Types of plots - bar graphs, pie charts,

• histograms

• Contour plots

 

7.  Machine Learning - I: Segmentation

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• K-Means Clustering

• Hierarchical Clustering

 

8.  Machine Learning - II: Regression

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• Simple Linear Regression

• Multiple Linear Regression

• Decision Tree – Regression

• Random Forest - Regression

 

9.  Machine Learning - III: Classification

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• Logistic Regression

• K-Nearest Neighbour

• Naïve Bayes

• Support Vector Machine

• Decision Tree – Classification

• Random Forest - Classification

 

10.  Machine Learning - IV: Recommendation System

 

• Association Mining Rule (Market Basket Analysis)