Professional Certification
in Python Data Science
 
Hands-On Learning: Real-World Applications
in Python Data Science
Expert-Led Courses and Industry-Recognized
Certification

Achieve Professional Certification
in Python Data Science.


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Python Data Science
Professional
Certification
Training Program
 
Start your data science journey
with Python and power up
your career today!
Get in Touch With Us
Program Duration
8-14 weeks
Learning Hours
8-12 hours per week
Program Delivery
Instructor Led
Learning Mode
Physical / Remote
The Professional Certification in Python Data Science (PCPDS) is designed for aspiring data scientists and professionals looking to enhance their analytical skills with Python.
This comprehensive program covers essential techniques in data analysis, machine learning, and data visualization, equipping you with the practical skills needed to excel in the field.
Ideal for individuals with a foundational knowledge of programming or data analysis, this certification is perfect for those seeking to advance their careers or transition into data science roles.
Core Highlights

Practical Assignments

We provide hands-on assignments that requires practical implementation.

Virtual Coaching Sessions

Online coaching sessions that happen over the phone, via video, or on a web platform.

1 Year Access to LMS

Get access to learning resources upto 1 year of class completion.

Live Project Experience

Hands-on learning and training gives participants the opportunity to experience real world situations.

Online Assessments

Participants can assess reflect on their own learning and their level/skills.

Access to Practice Labs

Access over 200 hours of premium content, including study materials, videos, and coding labs, all delivered through an advanced and intuitive platform.
Course Modules Covered in the Python Data Science program
Module 1 - An Overview of Python

An Overview of Python

  1. What is Python?
  2. Interpreted languages
  3. Advantages and disadvantages
  4. Downloading and installing
  5. Which version of Python
  6. Where to find documentation
  7. Python Comments
  8. Output to the screen
  9. Running Python Scripts
  10. Structure of a Python script
  11. Using the interpreter interactively
Module 2 - Getting Started

Getting Started
  1. Using variables
  2. Assigning value to multiple variables
  3. Expression
  4. Math operators
  5. String types: normal, raw and Unicode
  6. String operators
  7. Command line parameters
  8. Reading from the keyboard
Module 3 - Decision & Flow Control,

Decision & Flow Control
  1. About flow control
  2. Indenting is significant
  3. The if statements
  4. The nested if statements
  5. The elif statements
  6. The for loops
  7. The while loops
  8. Loop Controls - break and continue
  9. The range() function
  10. Arrays
Module 4 - Defining Functions

Defining Functions
  1. Syntax of function definition
  2. Formal parameters
  3. Global versus local variables
  4. Passing parameters and returning values
  5. Passing list of parameters
  6. Variable length arguments
  7. Lambda functions
  8. Passing function to another function
  9. Returning function
  10. Inner functions
    Module 5 - Working with Files

    Working with Files

    1. Text file I/O overview
    2. Opening a text file
    3. Reading text files
    4. Raw (binary) data
    5. Writing to a text file
    6. Opening Excel File
    7. Reading from Excel File
    8. Writing data into Excel File
    Module 6 - Sequence

    Sequence

    1. List overview
    2. List methods
    3. Tuple overview
    4. Tuple methods
    5. Dictionary overview
    6. Dictionary methods
    7. Set overview
    8. Set methods
    9. Fetching values
    10. Fetching keys
    11. Testing for existence of elements
    12. Deleting elements
    13. Set Operators
    Module 7 - Python Classes

    Python Classes

    1. About o-o programming
    2. Defining classes
    3. Class methods and data
    4. Constructors
    5. Objects
    6. Instance methods
    7. Instance data
    8. Destructors
    9. Interfaces
    10. Inheritances
    Module 8 - Errors and Exception Handling

    Errors and Exception Handling

    1. Dealing with syntax errors
    2. Exceptions
    3. Handling exceptions with try/except
    4. Cleaning up with finally
    Module 9 - Using Modules

    Using Modules

    1. What is a module?
    2. The import statement
    3. Function aliases
    4. Packages
    5. Installing Packages from PYPI
    6. Standard Modules – sys
    7. Standard Modules – math
    8. Standard Modules – time
    Module 10 - Regular Expressions

    Regular Expressions

    1. RE Objects and Pattern matching
    2. Parsing data
    3. Subexpressions
    4. Complex substitutions
    5. RE tips and tricks
    Module 11 - Standard Library

    Highlights of the Standard Library

    1. Working with the operating system
    2. Grabbing web pages
    3. Sending email
    4. Using glob for filename wildcards
    5. math and random
    6. Accessing dates and times with datetime
    7. Working with compressed files
    Module 12 - Databases

    Accessing Databases

    1. Selecting Data
    2. Inserting and Updating Data
    3. Deleting data
    4. Generic database API based on MySQL
    5. Using the Object Relational Mapper (SQLAlchemy)
    6. Working with NoSQL databases
    Module 13 - Data Distribution

    Data distribution

    1. Center
    2. Spread
    3. Shape – Symmetry, Number of peaks, Skewness, Uniform
    4. Unusual Features – Gaps, Outliers
    5. Measures of central tendency - Mean, Median, Mode, Midrange
    6. Measures of spread - Range, Variation, Standard deviation, Interquartile range
    7. Measures of shape - Empirical rule, Chebyshev's rule, Skewness, Kurtosis
    8. Measures of relative position – Quartiles, Percentiles, Midquartile
    Module 14 - Extract data from Website

    Extract data from Website - Beautiful soup

    1. Installing Beautiful Soup
    2. Installing a parser
    3. Making the soup
    4. Kinds of objects
    5. Navigating the tree
    6. Managing the tree
    7. Searching the tree
    8. Append the tree
    9. Insert inside the tree
    10. Extract, decompose, replace with,
    11. wrap and unwrap
    12. Pretty-printing
    13. Non-pretty printing
    14. Output formatters
    15. Get Text
    16. Output Encoding
    17. Unicode
    Module 15 - Selenium IDE

    Selenium IDE

    1. Selenium Overview
    2. Selenium IDE Introduction
    3. Downloading and Installing Selenium IDE
    4. Recording and Running a Simple Test
    5. Selenium IDE – Features
    6. Installing Useful Tools for Writing Tests
    7. Selenium Concepts
    Module 16 - Selenium Webdriver

    Selenium Webdriver

    1. Introduction to selenium webdriver
    2. Advantages of webdriver
    3. Downloading and configuring Webdriver
    4. Converting Selenium IDE test to programming language (Python)
    5. Detailed discussion about webdriver commands
    6. Handling different browsers
    7. Create our own methods in Webdriver
    8. Using RC commands from webdriver project
    Module 17 - Python for Data Analysis – NumPy

    Python for Data Analysis – NumPy

    1. Introduction
    2. Ndarray Object
    3. Data Types
    4. Array Attributes
    5. Array Creation Routines
    6. Array from existing data
    7. Numerical ranges
    8. Array Indexing and Slicing
    9. Advanced Indexing
    10. Iterating over Array
    11. Array Manipulation
    12. Arithmetic Operators
    13. Binary Operators
    14. String Functions
    15. Mathematical Functions
    16. Statistical Functions
    Module 18 - Python for Data Analysis – Pandas

    Python for Data Analysis – Pandas

    1. Introduction to Pandas
    2. Series
    3. DataFrames
    4. Missing Data
    5. Group By
    6. Merging Joining and Concatenating
    7. Operations
    8. Data Input and Output
    Module 19 - Python for Data Visualization

    Python for Data Visualization

    1. Matplotlib
    2. Seaborn
    3. Distribution Plots
    4. Categorical Plots
    5. Matrix Plots
    6. Grids
    7. Regression Plots
    8. Pandas Built-in Data Visualization
    9. Plotly
    10. Cufflinks
    11. Geographical Plotting
    12. Choropleth Maps
    Module 20 - Python for Data Analysis – SciPy

    Python for Data Analysis – SciPy

    1. Introduction
    2. Basic functions
    3. Special functions
    4. Integration
    5. Optimization
    6. Interpolation
    7. Fourier transforms
    8. Signal Processing
    9. Linear Algebra
    10. Sparse Eigenvalue Problems with ARPACK
    11. Compressed Sparse Graph Routines
    12. Spatial data structures and algorithms
    13. Statistics
    14. Multidimensional image processing
    Get Professionally Certified
    Upon successfully completing this program, participants will be awarded the Professional Certification in Python Data Science by International Council for Technology Certifications (ICTC).
    This award is a validation to the efforts taken to master the domain expertise that will set you apart from your competition.
    Be a part of the global network of data science professionals and join the community across sectors.
     
     



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