Data Science with Python

This course includes the fundamental python programming techniques such as lambdas, reading and
manipulating csv files, and the numpy library. The course also introduces data manipulation and cleaning
techniques using the popular python pandas data science library and introduce the abstraction of the
Series and Data Frame as the central data structures for data analysis, along with tutorials on how to
use functions such as group by, merge, and pivot tables effectively. 



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Real-time Industry Projects


Mastering data wrangling,
data exploration, visualization,
web scrapping,
hypothesis building and
mathematical computing


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The Python Data Science Course teaches you to master the concepts of Python programming. Through this Python Data Science training, you will gain knowledge in data analysis, Machine Learning, data visualization, web scraping, and Natural Language Processing. Upon course completion, you will master the essential tools of Data Science with Python.

In the past decade, the demand for data has increased exponentially. The industry has begun to realize the potential goldmine of summarized information collected online. The various processes in data science are collect, collate and disseminate. The industry is also investigating on various applications that can streamline the valuable information for analytics processes and making the data collection simple and efficient. The industry is expected to be worth over $128 billion by 2022, a predicted 36 per cent growth from 2016. With the Data Analytics Industry becoming dynamic, the prospects for someone looking to make Data Science as their career are high.

Although the amount of collected data is impressive, the data is useless without it is being analyzed and insights leading transformation. Without enough manpower to work out on the information, it is pointless collecting the data in the first place. Businesses are also starting to react to the data scientist shortage and are collaborating with other firms and educational establishments to close the gap before it becomes too large. Through this course, we have focused on the practical challenges that organizations are experiencing by merging disciplines to develop a teaching programmed that makes the link between business, management and data analytics.

This course includes the fundamental python programming techniques such as lambdas, reading and manipulating csv files, and the numpy library. The course also introduces data manipulation and cleaning techniques using the popular python pandas data science library and introduce the abstraction of the Series and Data Frame as the central data structures for data analysis, along with tutorials on how to use functions such as group by, merge, and pivot tables effectively. By the end of this course, participants will be able to take tabular data, clean it, manipulate it, and run basic inferential statistical analyses. 

After completing this course, you should be able to:

• Explore Python fundamentals, including basic syntax, variables, and types
• Create and manipulate regular Python lists
• Use functions and import packages
• Build Numpy arrays, and perform interesting calculations
• Create and customize plots on real data
• Supercharge with control flow, and get to know the Pandas DataFrame
• Use Python to read and write files
• Illustrate Supervised Learning Algorithms
• Identify and recognize machine learning algorithms around us

There are no prerequisites for this course but python knowledge with a little programming background is preferred.

This course "Data Science with Python" is intended for learners who have basic python knowledge and wants to apply statistics, machine learning, information visualization, social network analysis, and text analysis techniques to gain new insight into data.

Data Scientist, Data Analyst, Python Data Science Programmer, Analytics, Data Engineer and alike

Students will work on real-time issues and solving those issues by covering all the key aspects of data extraction, cleaning, and visualisation to model building and tuning. Students will also get the option of choosing the domain/industry dataset he/she wants to work on from the options available. This project is given to showcase to potential employers as a testament to his/her learning.
Project 1: Products recommendation prediction for AliBaba
Recommendation engines proved to be of great use for the retailers as the tools for customers’ behavior prediction. The retailers tend to use recommendation engines as one of the main leverages on the customers’ opinion. Providing recommendations enables the retailers to increase sales and to dictate trends. Alibaba, one of the leading US-based e-commerce companies, recommends products within the same category to customers based on their activity and reviews on other similar products. Alibaba would like to improve this recommendation engine by predicting ratings for the non-rated products and add them to recommendations accordingly.
Project 2: Improving customer experience for Telekom Malaysia
Description: Telekom Malaysia, one of the leading Malaysian-based telecommunication company that has been monopolising the Malaysian market telecommunication sector wants to improve customer experience by identifying and acting on problem areas that lower customer satisfaction. The company is also looking for key recommendations that can be implemented to deliver the best customer experience.
Project 3: Customer churn prevention
Acquiring a customer is a challenging task. Keeping the customer engaged requires a lot of effort as well. Accurate diagnosis of the customer's behavior and enabling alerts highlight the customers at a risk defecting. Smart data platforms can bring together customer transactions data and data from real-time communication streams to disclose the insights concerning customers feelings about the services. This allows immediate addressing the satisfaction-related issues and churn prevention.
Workforce Analytics
Project 4: Attrition Analysis for Hershey
Attrition in human resources refers to the gradual loss of employees over time. In general, relatively high attrition is problematic for companies. HR professionals often assume a leadership role in designing company compensation programs, work culture and motivation systems that help the organization retain top employees. Looking into how Hershey Used Data to Increase Retention Rates and Improve Workforce Planning by using workforce data to create a predictive retention model that helps identify flight risks at an individual and macro scale, allowing them to boost retention with targeted support and plan their talent needs months ahead of time to ensure they’re fully staffed when they need it most.

Project 5: IBM, one of the leading US-based IT companies, would like to identify the factors that influence attrition of employees. Based on the parameters identified, the company would also like to build a logistics regression model that can help predict if an employee will churn or not.
Project 5: Predict accurate sales for all stores of FashionValet, one of the leading Malaysian leading retail stores, considering the impact of promotional markdown campaigns. Check if macroeconomic factors like CPI, unemployment rate, etc. have an impact on sales
FashionValet runs several promotional markdown campaigns throughout the year. The markdowns precede prominent public holidays, such as all festivities in Malaysia. The weeks including these holidays are weighed five times higher in valuation than non-holiday weeks. The business is facing a challenge due to unforeseen demand, resulting in stocks running out at times due to inaccurate demand estimation. The macroeconomic factors like CPI, Unemployment Index, etc. also play an important role in predicting the demand, but the business hasn’t been able to leverage these factors yet. As a part of this project, create a model to highlight the effects of markdowns on holiday weeks.
Project 6: Learn how to Healthcare industry leaders make use of Big Data to leverage their business with the use of predictive analytics tools and obtain quick diagnosis to avoid readmissions or faster treatments.
Predictive analytics can be used in healthcare to mediate hospital readmissions with having insignt on quick diagnosis. In healthcare and other industries, predictors are most useful when they can be brought into action. This is vital for the prediction of inherited diseases. For instance, the patients at risk of developing a specific disease (e.g. diabetes) can benefit from preventive care with the use of fitness trackers and smartwatches to colelct and analyse information aout their heart rhythm and physical activity. As a result, this process is able to help doctors or medical specialists create flexible databases. After that, a computer with artificial experience may make suggestions for each patient according to the information collected from other human beings. Performing an analysis of information collected by trackers by neural networks allows patients to figure out the disease which as a result, patients can take in-time measures to predict and prevent it. Now using Big Data doctors are able to predict the results of their treatment, considering what kind of lifestyle the patient leads.
Project 6: Understand how the Insurance leaders like Berkshire Hathaway, AIG, AXA, etc. make use of Data Science by working on a real-life project based on Insurance.
Use of predictive analytics has increased greatly in insurance businesses, especially for the biggest companies, according to the 2013 Insurance Predictive Modeling Survey. While the survey showed an increase in predictive modeling throughout the industry, all the respondents from companies that write over $1 billion in personal insurance employ predictive modeling, compared to 69% of companies with less than that amount of premium.
Project 7: See how banks like Citigroup, Bank of America, ICICI, HDFC, etc. make use of Data Science to stay ahead of the competition.
Description: A Portuguese banking institution ran a marketing campaign to convince potential customers to invest in a bank term deposit. Its marketing campaigns were conducted through phone calls, and sometimes the same customer was contacted more than once. Student’s job is to analyze the data collected from the marketing campaign.

Course Outline

• Introduction to the Course
• Environment Set-Up
• Virtual Environments
• Data types and Operators
• Integers, Floats, Strings, Bytes, Tuples and Lists
• Dictionaries and Ordered Dictionaries
• Sets and frozen sets
• Flow control - if, elif statements
• Flow control - while loops
• Creating and using functions
• Creating modules and packages
• Distributing code to repositories  

• Creating Classes
• Creating Objects and Instances
• Data Encapsulation
• Class Inheritance
• Multiple Inheritance
• Decorators  

• Handling Exception
• Raising exceptions
• Writing tests cases
• Executing tests
• Checking code coverage by tests  

• Accessing different types of files
• File handling principles
• Creating and reading Files
• Updating Files
• Deleting files
• Text Files
• CSV Files
• Microsoft Word
• Microsoft Excel
• Regular Expressions
• Extracting data from text files using Regular Expressions
• Creating and deleting directories
• Listing and searching for files  

• Selecting Data
• Inserting and Updating Data
• Deleting data
• Generic database API based on MySQL
• Using the Object Relational Mapper (SQLAlchemy)  
• Working with NoSQL databases 

• Introduction 
• Ndarray Object
• Data Types
• Array Attributes
• Array Creation Routines
• Array from existing data
• Numerical ranges
• Array Indexing and Slicing
• Advanced Indexing
• Iterating over Array
• Array Manipulation
• Arithmetic Operators
• Binary Operators
• String Functions
• Mathematical Functions
• Statistical Functions  

• Introduction
• Basic functions
• Special functions
• Integration
• Optimization
• Interpolation
• Fourier transforms  
• Signal Processing
• Linear Algebra
• Sparse Eigenvalue Problems with ARPACK
• Compressed Sparse Graph Routines  
• Spatial data structures and algorithms
• Statistics
• Multidimensional image processing
• File IO  

• Introduction to Pandas
• Series
• DataFrames
• Missing Data
• Groupby
• Merging Joining and Concatenating
• Operations
• Data Input and Output  

• Matplotlib
• Seaborn
• Distribution Plots
• Categorical Plots
• Matrix Plots
• Grids
• Regression Plots
• Pandas Built-in Data Visualization
• Plotly and Cufflinks  
• Geographical Plotting
• Choropleth Maps 

• Introduction
• Machine Learning with Python
• Linear Regression
• Logistic Regression
• K Nearest Neighbours
• Decision Trees and Random Forests
• Support Vector Machines
• K Means Clustering  

• Natural Language Processing Theory
• NLP with Python
• NLP Project Overview
• NLP Project Solutions  

• Neural Network Theory
• What is TensorFlow
• Installing Tensorflow
• TensorFlow Basics
• MNIST with Multi-Layer Perception
• Tensorflow with ContribLearn
• Deep Learning Project  



Mr Ajith Kumar
Enterprise Architect | Big Data Consultant | Analytics SME

► 22 Yrs of Technology & Industry Experience
► Data Science and Machine Learning Consultant
► Center Of Excellence member for SOA & Big Data
► Telecom Consultant & SME for SDP, BPM, EMM & Big Data
► Strategy & IT Transformation Consultant for   Telecom & Banking


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