Python for Data Science
Build your foundation in data science with Python—one of the most in-demand skills in tech today. This beginner to intermediate-level course takes you from the basics of programming in Python to working with powerful libraries. You’ll learn data collection, cleaning, visualization, analysis, and even develop simple machine learning models—equipping you with real-world data skills used across industries.
This course is ideal for:
- Aspiring Data Scientists and Analysts Individuals who want to build a strong foundation in data science using Python and transition into data-driven roles.
- Beginners in Python Programming Learners with little or no coding experience who want to start with Python and move into practical data science applications.
- Students and Recent Graduates Those pursuing degrees in computer science, statistics, mathematics, or related fields who want hands-on training in Python and data analysis.
- Working Professionals Seeking a Career Switch IT professionals, engineers, or business analysts aiming to reskill or pivot into data science, machine learning, or AI roles.
- Researchers and Academics Individuals in research or academic roles who want to leverage Python for statistical modeling, data analysis, and visualization.
- Anyone Curious About Data Science Enthusiasts who want to understand how data is collected, processed, visualized, and used to make predictive decisions with Python.
Highlights
Upgrade your career with top notch training
- Enhance Your Skills: Gain invaluable training that prepares you for success.
- Instructor-Led Training: Engage in interactive sessions that include hands-on exercises for practical experience.
- Flexible Online Format: Participate in the course from the comfort of your home or office.
- Accessible Learning Platform: Access course content on any device through our Learning Management System (LMS).
- Flexible Schedule: Enjoy a schedule that accommodates your personal and professional commitments.
- Job Assistance: Benefit from comprehensive support, including resume preparation and mock interviews to help you secure a position in the industry.
Outcomes
By the end of this course, participants will be equipped with:
- Proficiency in Python Programming: A solid understanding of Python programming fundamentals, including syntax, data types, control structures, functions, and object-oriented programming, equipping them to write efficient Python code.
- Data Manipulation Techniques: Master data manipulation using libraries such as Pandas and NumPy, allowing them to clean, transform, and analyze data from diverse sources effectively.
- Data Visualization: Learn how to create visualizations to represent data insights using libraries like Matplotlib, enhancing their ability to communicate findings clearly and effectively.
- Statistical Analysis: Understand the basics of statistical concepts, including descriptive statistics, probability distributions, and inferential statistics, enabling participants to apply statistical methods to analyze data.
- Introduction to Machine Language: Gain foundational knowledge of machine learning concepts and algorithms, including supervised and unsupervised learning, and how to implement basic machine learning models.
Course Content
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About
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Key Learnings
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Pre-requisites
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Job roles and career paths
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Curriculum
Data science involves using data to find patterns, make predictions, and help make better decisions. It combines skills from statistics, computer science, and domain knowledge to analyze and interpret complex data.
This course provides a comprehensive introduction to data science using Python. You'll start with Python basics and explore key libraries used in data science. The training covers essential concepts in statistics and probability, data pre-processing techniques, and effective data visualization methods. You'll also learn how to develop and evaluate machine learning models to extract valuable insights from your data. Ideal for anyone looking to build a solid foundation in data science.
By the end of this course, you will gain a strong foundation in both Python programming and data science concepts.
- Master the fundamentals of Python programming, including syntax, data types, operators, and control flow structures, which form the foundation for coding in data science.
- Learn to use the Pandas library for data manipulation, including data cleaning, transformation, and handling missing values.
- Gain proficiency in using NumPy for numerical operations and array manipulations. Understand how to perform complex mathematical computations efficiently.
- Explore various data visualization libraries, such as Matplotlib, to create informative graphs and charts that effectively communicate data insights.
- Understand key statistical concepts and techniques, including descriptive statistics, hypothesis testing, and probability distributions, essential for analyzing and interpreting data.
- Get acquainted with machine learning concepts, including the difference between supervised and unsupervised learning, and explore algorithms like regression, classification, and clustering.
- Learn how to develop machine learning models, including training, testing, and evaluating model performance with metrics such as accuracy, precision, and recall.
- Basic Computer Skills: Participants should have a general understanding of using computers, including file management, navigating the operating system, and using web browsers effectively.
- Familiarity with Python: While prior programming experience is not required, a basic understanding of programming concepts using Python is beneficial. You can take our < Programming Essentials using Python> course prior to this course.
- Basic Mathematics: A basic understanding of mathematics, particularly statistics, is helpful, as data science relies heavily on mathematical concepts and statistical analysis.
This training will equip you for the following job roles and career paths:
- Data Scientist
- Data Analyst
- Machine Learning Engineer
- Data Engineer
- Business Intelligence Developer
Module1. Introduction to Data Science
- What is Data Science?
- Collecting Data
- Storing Data
- Processing Data
- Describing Data
- Statistical Modelling
- Algorithmic Modelling
- How AI, ML and Data Science related
Module 2. Introduction to Python
- Why Python
- Python Features
- Installing Python
- Python IDE
- Jupyter Notebook
- Google Colab
Module 3. Python Refresher
- Basic Data types
- Operators
- Input/Output statements
- Control Statements
- Functions
- Strings
- List and Tuples
- Dictionaries
- File Handling
- Exceptions
- Object Oriented programming
Module 4. Python Libraries for Data Science
- NumPy (Numeric Python)
- Pandas
- Matplotlib
- Scikit
Module 5. Introduction to Statistics
- What is Statistics
- Quantitative and Categorical Data
- Grouping and Displaying Data
- Histogram/ Stem Plot/Time Plot
- Summary Statistics
- Measures of Centre
- Measure of Spread
- Outliers
- Inter Quartile Range
- Percentile
- Box and Whisker Plots
Module 6. Probability
- What is Probability
- Random Variable
- Probability Distributions
- Binomial Distribution
- Poisson Distribution
- Standard Normal Distribution
- Density Curve
- Symmetry and Skewness
- Normal Distributions
- 68-95-99.7 Rule
- Z-Score
Module 7. Data Pre-processing with Python
- Python Libraries for Pre-processing
- Data Cleaning or Data Wrangling
- Dealing with Missing Values
- Data Formatting
- Data Normalization
- Binning
- One-hot encoding
Module 8. Data Visualization with Python
- Introduction to Data Visualization
- Matplotlib Pandas Integration
- Histogram
- Line Plot
- Bar Charts
- Area Plots
- Pie Charts
- Box Plots
- Scatter Plots
Module 9. Data Analysis with Python
- Exploratory Data Analysis
- Analysis of Variance
- Positive and Negative Correlation
- Pearson Correlation
- Regression
Module 10. Model Development and Evaluation
- Introduction to Scikit Library
- Simple Linear Regression
- Multiple Linear Regression
- Model Evaluation
- Prediction and Decision Making
Module 11. Introduction to Machine Learning
- What is Machine Learning
- Supervised Machine Learning
- Unsupervised Machine Learning
- Recommender System
- Deep Learning
Demand for Python for Data Science
The demand for the Python for Data Science course is influenced by several key trends in the tech industry and the job market. The demand for data scientists and professionals with data analysis skills continues to grow across various industries, including finance, healthcare, marketing, and technology. Organizations are increasingly leveraging data to drive business decisions. Data science roles are among the fastest-growing job categories, with positions for data scientists, analysts, and engineers being widely advertised. This creates a significant need for professionals with data science skills.
FAQs
Data science is the field of study that uses scientific methods, algorithms, and systems to analyze and interpret complex data. It combines techniques from statistics, computer science, and domain expertise to extract meaningful insights, make predictions, and inform decision-making
You will learn Python basics, statistical analysis, data pre-processing, data visualization, and how to build and evaluate machine learning models.
The course is designed to be completed in approximately 48 hours, which includes 24 hours of instructor-led training and 24 hours of student practice.
Participants should have a general understanding of computer usage. While this course provides a refresher on Python essentials, prior familiarity with Python basics will be beneficial.
Yes, participants will receive a certificate of completion for the “Python with Data Science” course. This certification may be beneficial for your resume or LinkedIn profile.
The course covers essential topics including Data science, Python programming essentials, Statistical analysis, data manipulation using Pandas, data visualization with Matplotlib, and an introduction to machine learning concepts.
Yes, the course is offered in an online format, allowing you to participate from anywhere with a stable internet connection.
Yes, the course includes hands-on coding exercises, quizzes, to help reinforce learning and assess your understanding.
To enroll in this course, please email us at enroll@ohiocomputeracademy.com
Yes, discounts may be available for group registrations. Please contact us at enroll@ohiocomputeracademy.com for more details on group pricing options.
Participants will have access to instructor support throughout the course, along with resources to facilitate learning, including assignments, and exercises.
Yes, the course is open to anyone interested in learning Python for data science, whether you are a student, a working professional, or looking for a career change.
