Programming for Data Science Training

Live Online & Classroom Enterprise Training

Programming for Data Science involves using languages like Python, R, and SQL to analyze, visualize, and interpret data. It includes data manipulation, statistical modeling, and machine learning to derive meaningful insights for decision-making.

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What is Programming for Data Science Training about?

This course provides a hands-on introduction to programming for data science, covering key programming languages, tools, and techniques used in data analysis, visualization, and machine learning. Participants will learn how to manipulate datasets, perform exploratory data analysis, automate workflows, and build predictive models using Python, SQL, and essential data science libraries.

What are the objectives of Programming for Data Science Training ?

Understand the fundamentals of Python and SQL for data science
Use Pandas and NumPy for data manipulation and analysis
Perform data cleaning, transformation, and exploratory data analysis (EDA)
Create visualizations with Matplotlib and Seaborn
Write SQL queries to extract and analyze data from databases
Apply basic machine learning techniques using Scikit-Learn

Who is Programming for Data Science Training for?

Aspiring Data Scientists & Analysts
Software Engineers & Developers looking to enter data science
Business Analysts & Decision Makers
Anyone interested in programming for data analysis

What are the prerequisites for Programming for Data Science Training?

Basic understanding of mathematics and statistics
Familiarity with spreadsheets or databases (helpful but not required)
No prior programming experience needed

Available Training Modes

Live Online Training

4 Days

Self-Paced Training

40 Hours

Course Outline Expand All

Expand All

Module 1: Introduction to Data Science and Programming

What is Data Science?

Overview of Python and SQL in data science

Setting up your environment: Jupyter Notebook, Anaconda, Google Colab

Module 2: Python for Data Science

Introduction to Python programming (variables, data types, loops, functions)

Working with lists, dictionaries, and tuples

Writing and organizing Python scripts for automation

Module 3: Data Manipulation with Pandas & NumPy

Loading and exploring datasets using Pandas

Performing data cleaning and transformation

NumPy for numerical operations and matrix computations

Module 4: Data Visualization with Matplotlib & Seaborn

Creating line plots, bar charts, scatter plots, and histograms

Customizing and styling data visualizations

Advanced visualizations with Seaborn (heatmaps, pair plots, KDE plots)

Module 5: SQL for Data Science

Introduction to relational databases and SQL

Writing SELECT, JOIN, GROUP BY, and aggregate functions

Connecting Python with SQL databases (SQLite, PostgreSQL, MySQL)

Module 6: Exploratory Data Analysis (EDA)

Identifying trends, patterns, and anomalies in data

Handling missing values and outliers

Statistical analysis and hypothesis testing

Module 7: Introduction to Machine Learning

Overview of supervised and unsupervised learning

Implementing linear regression and classification models using Scikit-Learn

Model evaluation: accuracy, precision, recall, and F1-score

Who is the instructor for this training?

The trainer for this Programming for Data Science Training has extensive experience in this domain, including years of experience training & mentoring professionals.

Reviews

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