Python for Data Science
Overview
Course Objective
In this course, you will learn the following:
• Identify types of data that can be extracted
• Cleaning and preparing data
• Common approach in analyzing data
• Visualization using Python
• Python machine learning
Who Should Attend
Prerequisites
Basic knowledge of programming is preferable.
Training Calendar
Intake
Duration
Program Fees
Module
Module 1
• Introduction to Python and Data Science
• Installing and setting up the Python environment
• Basic Python syntax and data types
• NumPy, sets, list, and tuples data storage
• Control structures in Python
• Creating and using functions, date and time functions
• Laboratory exercise – My first Python program
Module 2
• Comprehension of the use of lists and indexing data with dictionaries
• Sub-setting (Lists, Matching, Handling Missing Values)
• Reading and writing real-world data (text file, CSV and SQL)
• Introduction to pandas and managing Data Frames
• Using pandas for data conditioning
• Laboratory exercise – Working with Python pandas data frame
Module 3
• Shaping data for analysis
• Handling raw text, using bag of words, TF-IDF and DTM shaping
• Sub-setting observations & variables, summarizing data
• Laboratory exercise – Getting, format (sub-setting) and store data
• Feature creation, combining variables
• Understanding binning, discretization and data distribution
• Laboratory exercise – Preparing extracted data analysis
Module 4
• Introduction to MatPlotLib
• Exploratory graphs in Python
• Visualizing data
• Laboratory exercise – Visualizing data
• Wrangling data and reducing dimensionality (feature selection)
• Basic clustering of data and handling outliers
• Laboratory exercise – Presenting analytics using Python
Module 5
• Regression
• Association Rule and Frequent Itemset Mining
• Knn, Decision Trees and Random Forest
• Support Vector Machines
• Text processing using NLTK
• Laboratory exercise – Text classifier
• Wrap-up
FAQs
General Questions:
Q: What is the Python for Data Science course about?
This 5-day course provides a comprehensive introduction to using Python for data science. It covers the essentials of data extraction, preparation, exploratory data visualization, and machine learning algorithms. By the end of the course, participants will be equipped with the foundational skills to start their journey as a data scientist.
Q: Who should attend this course?
The course is designed for IT professionals, data analysts, data scientists, and anyone interested in learning Python programming. It is suitable for those with some experience in programming, data management, and business reporting. The ideal class size is between 10 and 12 participants.
Q: What are the prerequisites for this course?
Participants should have a basic understanding of programming. While prior experience with Python is not necessary, a familiarity with programming concepts will be helpful.
Q: How long is the course?
The course lasts 5 days, with instructor-led sessions focusing on practical, hands-on exercises and applications of Python in data science.
Q: What key topics are covered in this course?
The course covers the following topics:
Introduction to Python and data science
Python syntax, data types, and control structures
Data manipulation with NumPy, Pandas, and DataFrames
Data visualization using Matplotlib
Machine learning basics, including regression, decision trees, and clustering
Text processing using NLTK
Q: Will I receive a certification after completing the course?
Yes, participants will receive a certificate of completion, demonstrating their proficiency in using Python for data science.
Program Content & Skills:
Q: What foundational Python concepts will I strengthen in this course?
You’ll strengthen essential Python concepts such as data types, control flow, working with sequences and mappings, file input/output, and program structure. These fundamentals will provide a strong foundation for tackling more advanced tasks in data science.
Q: How does the course help me apply Python to real-world problems?
Through hands-on exercises, you’ll create Python programs for real-world tasks such as reading and processing data from various sources, performing data analysis, and visualizing results. These practical applications will prepare you for roles in data science, machine learning, and more.
Q: What skills will I develop in managing and structuring Python applications?
You’ll learn to structure your Python applications effectively using functions, modules, and packages. The course also covers object-oriented programming, exception handling, and code maintenance techniques, including basic testing and debugging to ensure your applications are robust and efficient.
Q: Will I learn how to work with different data and systems in Python?
Yes, you will work with various Python data structures such as lists, dictionaries, and sets. You’ll also learn to manipulate text, use regular expressions, and perform data analysis using libraries like NumPy and Pandas, which will give you the skills to handle diverse types of data.
Q: How does this course prepare me for using Python professionally?
This course equips you with the core skills needed for professional Python use, covering key areas like functions, file handling, object-oriented programming, and working with libraries such as NumPy and Pandas. These skills will prepare you for a wide range of applications, from automation and data processing to machine learning and data science.
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