About the Course
This essential course provides a robust foundation in Python programming specifically tailored for Data Science and Data Analysis. You will master the core Python concepts needed to effectively work with data, including using fundamental structures, controlling program flow, and implementing functions. A major focus is placed on the most critical data science libraries: NumPy for high-performance numerical operations and Pandas for efficient data manipulation and cleaning. This is the ideal starting point for anyone looking to build a career in data science, analytics, or machine learning.
Audience Profile
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Aspiring Data Scientists and Data Analysts seeking Python proficiency.
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Professionals in fields like finance, marketing, or research who need to analyze large datasets.
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Individuals with basic programming experience looking to specialize in data-centric applications.
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Anyone preparing for more advanced courses in machine learning or statistical modeling.
Learning Objectives
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MASTER PYTHON FUNDAMENTALS: Understand core Python syntax, data structures (lists, dictionaries, tuples), and control flow mechanisms.
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UTILIZE NUMPY: Effectively use the NumPy library for vectorization, array manipulation, and high-performance numerical computing.
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APPLY PANDAS: Perform robust data cleaning, manipulation, and analysis using the powerful features of the Pandas library (DataFrames and Series).
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PERFORM DATA I/O: Learn to efficiently read and write data from various formats, including CSV, Excel, and JSON files.
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WRITE CLEAN CODE: Develop modular, readable, and efficient Python code using functions and standard coding practices.
Prerequisites
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Basic computer literacy and familiarity with command-line interfaces.
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No prior programming experience is required, but an understanding of basic mathematics is helpful.
What’s included?
- Authorized Courseware
- Intensive Hands on Skills Development with an Experienced Subject Matter Expert
- Hands-on practice on real Servers and extended lab support 1.800.482.3172
- Examination Vouchers & Onsite Certification Testing- (excluding Adobe and PMP Boot Camps)
- Academy Code of Honor: Test Pass Guarantee
- Optional: Package for Hotel Accommodations, Lunch and Transportation
With several convenient training delivery methods offered, The Academy makes getting the training you need easy. Whether you prefer to learn in a classroom or an online live learning virtual environment, training videos hosted online, and private group classes hosted at your site. We offer expert instruction to individuals, government agencies, non-profits, and corporations. Our live classes, on-sites, and online training videos all feature certified instructors who teach a detailed curriculum and share their expertise and insights with trainees. No matter how you prefer to receive the training, you can count on The Academy for an engaging and effective learning experience.
Methods
- Instructor Led (the best training format we offer)
- Live Online Classroom – Online Instructor Led
- Self-Paced Video
Speak to an Admissions Representative for complete details
| Start | Finish | Public Price | Public Enroll | Private Price | Private Enroll |
|---|---|---|---|---|---|
| 12/8/2025 | 12/10/2025 | ||||
| 12/29/2025 | 12/31/2025 | ||||
| 1/19/2026 | 1/21/2026 | ||||
| 2/9/2026 | 2/11/2026 | ||||
| 3/2/2026 | 3/4/2026 | ||||
| 3/23/2026 | 3/25/2026 | ||||
| 4/13/2026 | 4/15/2026 | ||||
| 5/4/2026 | 5/6/2026 | ||||
| 5/25/2026 | 5/27/2026 | ||||
| 6/15/2026 | 6/17/2026 | ||||
| 7/6/2026 | 7/8/2026 | ||||
| 7/27/2026 | 7/29/2026 | ||||
| 8/17/2026 | 8/19/2026 | ||||
| 9/7/2026 | 9/9/2026 | ||||
| 9/28/2026 | 9/30/2026 | ||||
| 10/19/2026 | 10/21/2026 | ||||
| 11/9/2026 | 11/11/2026 | ||||
| 11/30/2026 | 12/2/2026 | ||||
| 12/21/2026 | 12/23/2026 | ||||
| 1/11/2027 | 1/13/2027 |
Curriculum Python Fundamentals for Data Science
Module 1.0 – Python Basics for Data Science Setting up the Python environment (Anaconda/Jupyter). Core Python syntax, variables, and basic arithmetic operations. Understanding Python’s fundamental data structures: Lists, Tuples, Sets, and Dictionaries.
Module 2.0 – Program Flow and Functions Implementing conditional logic (if/elif/else). Using loops (for and while) to iterate over data. Defining and calling custom functions for code reusability and modularity.
Module 3.0 – Introduction to NumPy for Numerical Computing Understanding the NumPy Array structure and its performance advantages. Array creation, indexing, slicing, and reshaping techniques. Vectorization and high-performance mathematical operations.
Module 4.0 – Data Manipulation with Pandas (Part 1) Introduction to the Pandas library and its primary structures: Series and DataFrames. Reading and writing data from files (CSV, Excel). Basic data inspection, selection, and indexing (loc/iloc).
Module 5.0 – Data Cleaning and Preparation with Pandas (Part 2) Handling missing data (NaN values): identification, removal, and imputation. Data transformation: filtering, sorting, and applying functions. Combining DataFrames: Merging, joining, and concatenation.
Module 6.0 – Basic Data Aggregation and Analysis Grouping data using the GroupBy feature. Calculating descriptive statistics (mean, median, standard deviation). Pivoting and reshaping data for analysis.
