Course content

Python is one of the most time-tested, flexible and reliable open-source languages which is easy to learn and use. Python also has efficient and powerful libraries for analysis and data manipulation. For specialized computing, Python is used in varying domains such as oil and gas, finance, Physics and many more. The inbuilt debugger in Python makes debugging a relatively effortless task. The usage of Python increases programmer productivity and also improves the end programs greatly. With Python, one will be able to master web and game development and gain a strong foothold as a programmer in the IT domain. On successful completion of Python training always assures a job in Big Data Hadoop environment for relatively higher salaries.
Did you know?
  1. Python’s design and libraries provide 10X productivity when compared to C, C++, or Java.
  2. Python is one of the most popular object-oriented languages available and can be learnt fast and deployed easily. It can run on different platforms such as Mac OS, Windows and Unix/Linux making it highly suitable for the Data Analytics domain.
  3. Python is free to use for all commercial products, due to its OSI-approved open-source license.
  4. Python has evolved as the most preferred language for Data Analytics in the current IT marketplace and analysing the current and recent search trends on Python indicate that Python is the next major innovation and is a must for Data Analytics Professionals.

Why learn and get certified in Python?
  1. According to the leading global job portal indeed.com, a senior Python Developer in the United States can command a salary of up to $102,000.
  2. Python has the largest year-on-year job demand growth rate at 19% as most of the companies like Google, Yahoo!, Disney, Nokia and IBM uses Python
  3. Programmers across the world prefer Python because of its speed and ease of use. Python is so efficient in application that it reduces development time to half due to its simple and easy to read syntax and effortless compilation feature.
  4. According to HackerRank, which provides a competitive platform for coders, out of a total of 38 programming languages worldwide, 13.95% of all code submitted was in Python.
Course Objective
After the completion of this course, Trainee will:
  1. Master the basic and advanced concepts of Python
  2. Learn about File and Sequence Operations
  3. Understand Python scripts on Unix/Windows, Python editors and IDEs
  4. Learn about the significance and installation
  5. Learn how to use and create functions, sorting different elements, Lambda function, error and exception handling techniques and Regular expressions using modules in Python
  6. Understand Socket programming by working on real-time projects such as FAQ Chat Application and Port Scanning Software
  7. Learn working with MySQL database by installing MySQL-server, creating database and connecting MySQL and Python
  8. Master Python Frameworks such as DJANGO and FLASK
Pre-requisites
  1. Prior programming experience is desirable but not necessary along with familiarity with basic concepts like variables and scope, functions and flow control
  2. Basic knowledge of object-oriented programming concepts is preferred but not mandatory
Who should attend this Training?
This certification is highly suitable for a wide range of professionals either aspiring to or are already in the IT domain, such as:
  1. Professionals aspiring to make a career out of Big Data Analytics utilizing Python
  2. Software Professionals
  3. Analytics Professionals
  4. ETL Developers
  5. Project Managers
  6. Testing Professionals
  7. Other professionals who are looking for a solid foundation on open-source general purpose scripting language also can opt this training
Module 1 :  Python Programming Language
Part A :  Python Basic  Concepts
  1. Introduction to Python and its involvement with Data Science
  2. Understanding Object Orientation Programming
  3. Installation: Python 3.6 or later version, pip, iPython, Sublime Text Editor, Anaconda(Jupyter and Spyder)
  4. Python Identifiers, Naming Conventions, Variables and Types
  5. Defining Functions, Classes and Methods
  6. Understanding Indentation
  7. Executing sample programs in all Editors
  8. Difference Between Functions and Methods
  9. How to use Python Functions and Methods
  10. Decision making through conditions and Loops
  11. Declaring instances and Workout its accessibility
  12. Understanding global and local variables in python
  13. Instantiating Classes and flow of execution
  14. Accessing Methods, Variables, Global variables and Functions
  15. Working with self and super keywords
  16. Object String representation through __str__ and __repr__
  17. Constructors; Initialization; object: a base class
  18. Inheritance Concept; Overriding and Overloading concept
  19. Constructors with respect to inheritance
  20. Understanding __name__ == ‘__main__’
  21. Exceptions:
  22. Overview of exception
  23. Raising common causing exceptions
  24. Exception Hierarchy
  25. Raising exception at calling method
  26. Handling exceptions through try, except, else and finally
  27. Exception propagation
  28. Customized Exceptions
Part B: Data Structures:
  1. List: Creating, Accessing, Slicing, Manipulating lists, Built-in Functions & Methods in list, Iterating & Enumerating list data and Working with Nested lists.
  2. Tuple, Set and Dictionaries (same above all operations)
  3. Handling conversions of sample data with Data Structures
Part C: Regular Expressions in Python
  1. Patterns, searching, Modifiers, flags
  2. Working with examples to find specific strings, phone numbers, email addresses and filtering html data with regular expressions
  3. File I/O
  4. Working with text files and .csv
  5. Reading and Writing data to the files
  6. Importing required packages to work with .csv
Module2 : Statistics – Probabilities  and Linear Algebra
  1. Statistical thinking in Python and approach of Data Analysis
  2. Fundamental statistics terms and its definitions
  3. Applying basic statistics in Python with NumPy
  4. Cumulative Distribution functions
  5. Modelling Distributions
  6. Graphical exploratory data analysis with Python
  7. Probability theories:
  8. Ranges, Mean, Variance, Standard Deviation and various distributions
  9. Mass and Density functions
  10. Kernel density estimation
  11. Understanding Bayes theorem and predictions*
  12. Estimation
  13. Sampling distributions, bias and Exponential distributions
  14. Hypothesis testing
  15. Hypothesis Test
  16. Testing Correlation and Proportions
  17. Chi-Squared Tests
  18. Errors, Power and Replication
  19. NumPy: N-dimensional array operations
  20. Array creations, conversions, dimensional understandings, shaping, reshaping, generating sample large datasets, Linear algebra functionalities and numerical operations etc…
  21. SciPy: High-level Scientific Computing
  1. Linear Algebra operations
  2. Interpolation
  3. Optimization and fit
  4. Statistics and random numbers
  5. Numerical Integration
  6. Fast Fourier transforms
  7. Signal processing and image manipulation
Module3 : Data Mining & Data Analytics (Data Harvesting, Cleansing, Analyzing and Visualizing)
Part A :Pandas and NumPy Functionalities:
  1. Introduction
  2. Pandas DataFrame basics
  3. Understanding data, looking at columns, rows and cells
  4. Subsetting Columns, Rows with methods
  5. Grouped and Aggregated Calculations
  6. Frequency Means and Counts
  1. Basic plot
  2. Pandas Data Structures
  1. Creating your own data (Series and DataFrame)
  1. Series (also called as Vector) Object operations
  1. Broadcasting and Scalar operations
  1. DataFrame Broadcasting (Vectorized)
  2. Making changes to Series and DataFrame
  1. Adding additional Columns ii.   Dropping values
  1. Exporting and Importing Data
Part B :  Introduction to Plotting:
  1. Introduction
  2. Matplotlib
  3. Statistical Graphics using matplotlib
  4. Univariate
  5. Bivariate
  6. Multivariate Data
  7. Seaborn Library Plotting methodology
  8. Univariate, Bivariate and Multivariate
  9. Pandas Objects Plotting
  10. Histogram, Density Plot, Scatterplot, Hexbin Plot and Boxplot
  11. Seaborn Themes and Styles
Part C : Data Manipulation:
  1. Data Assembly
  2. Concatenations and Merging Multiple datasets
  3. Missing Data:
  4. Introduction
  5. What is a NaN Value
  6. Working with merged data, user input values and Re-indexing
  7. Working with missing data
  8. Finding and Counting missing data
  9. Cleansing missing data
  10. Calculations with missing data
  11. Conclusion Understanding Multiple Observations (Normalization)
Part D : Data Munging:
  1. Understanding Data Types
  2. Converting types
  3. Categorical Data
  4. Convert to Category
  5. Manipulating Categorical Data
  6. Strings and Text Data
  7. String Subsettings
  8. String Methods
  1. String Formatting
  2. Apply and Groupby Operations:
  1. Introduction
  2. Functions
  3. Apply over a Series and DataFrame
  4. Apply- Column-wise and Row-wise operations
  1. Groupby Operation:
  1. Aggregate Methods and Functions
  1. The datetime Data Type:
  1. Python’s datetime Object
  2. Loading, Converting, Extracting Date components
  3. Date Calculations
  4. Datetime Methods
  5. Subsetting datetime, Date Ranges, Shifting Values, TimeZones
Module 4 : Machine Learning  (Data Modelling)
  1. Linear Models
  2. Linear and Multiple Regressions using statsmodels and sklearn
  3. Generalized Linear Models
  4. Logistic and Poisson Regressions using statsmodels and sklearn
  5. Survival Analysis
  1. Model diagnostics
  1. Residuals
  2. Comparing Multiple Models
  3. k-Fold Cross-Validation
  1. Regularization
  2. Clustering
  1. k-Means, Dimension Reduction with PCA (Principal Component Analysis)
  2. Hierarchical Clusterings
  3. Conclusions
Practical Data Analysis and Understandings
Data Science Interview Questions Discussions (2 sessions)
Note: Keeping main objective as “Understanding” All the above topics are covered with logical and programmatic approach in Python. Also please note that Content order is NOT compulsorily followed at the time of delivering subject and knowledge.

Mode of Training

Online

Total duration of the course

5 to 7 weeks

Training duration per day

50 mins - 90 mins

Communication Mode

Go to meeting, WEB-EX

Software access:

Software will be installed/Server access will be provided, whichever is possible

Material

Soft copy of the material will be provided during the training.

Training

Both weekdays and weekends

Training Fee

 $300