The Python environment encompasses the entire ecosystem in which Python code is developed, executed, and managed. It consists of an interpreter, libraries, tools, and resources that facilitate coding tasks. Python’s interactive nature allows developers to experiment and write code in its interpreter, while Integrated Development Environments (IDEs) like PyCharm and Jupyter Notebook offer enhanced coding experiences. While we introduce you to the environment, we have also prepared a few lessons for you in this module. You’d be learning:

√ Python Basics
√ String, list, indexing
√ Python Program Discussion in Loops
√ Function

Advanced Python delves into intricate programming concepts, optimizing code performance, and leveraging advanced libraries. Mastery of object-oriented principles facilitates elegant and modular code design. Here is what you’d be learning in this module:

√ Iteration Generator and File System
√ Exception Handling
√ Module and Packages
√ OOPS Concepts

Working with databases in Python involves using libraries like SQLAlchemy and psycopg2 for interacting with various database systems such as MySQL, PostgreSQL, and SQLite. Python’s database APIs facilitate connecting, querying, and modifying data. In this module, we’d be taking a dive into:

√ SQL
√ OOPS Discussion
√ Introduction to MongoDB
√ Working with Python and MongoDB
√ SQL lite, map, reduce, filter, zip

Working with pandas and NumPy in Python offers powerful data manipulation and analysis capabilities. Pandas provides DataFrame and Series structures, enabling easy handling of structured data. It facilitates tasks like data cleaning, transformation, and aggregation, essential for data preprocessing. Join us as we take you through:

√ Introduction to Pandas
√ Working with Pandas
√ Pandas Data Analysis
√ Numpy Methods

Working with graphs and charts in Python involves utilizing libraries like Matplotlib, Seaborn, and Plotly to visualize data effectively. Matplotlib offers diverse plotting options, enabling the creation of line plots, bar charts, scatter plots, and more.

√ Introduction to Graphs and Charts
√ Working with Graphs in Python

Introduction to Statistics
Statistics

Machine learning is a subset of artificial intelligence focused on developing algorithms that enable computers to learn and improve from data. It empowers systems to recognize patterns, make predictions, and adapt without explicit programming.

√ Introduction to Machine Learning
√ Linear Regression
√ Gradient Boosting
√ Clustering
√ Decision Tree
√ Project Deployment in Heroku, Azure, and AWS

Basic charts in Power BI, a data visualization tool, offer impactful insights. Bar charts present categorical data comparisons, while line charts reveal trends over time.

√ Basic Chart in Power BI Desktop
√ Funnel Chart, Donut Chart, Pie Chart, Ribbon Chart, Column Chart
√ Include and Exclude
√ Export Data from Visual

In Power BI, tables and matrices serve as fundamental data presentation tools. Tables present data in a structured grid format, displaying individual records and their attributes. Matrices extend tables by allowing grouping and hierarchies, providing a summarized view of data.

√ Table and Matrix in Power BI
√ Creating and Formatting a Table in Power BI
√ Conditional Formatting in Table
√ Conditional Formatting in Matrix

SQL (Structured Query Language) is a domain-specific language used for managing and manipulating relational databases. It provides a standardized way to interact with databases, enabling tasks such as data retrieval, insertion, updating, and deletion.

√ Introduction to SQL
√ Database Architecture
√ Data Definition Language
√ Data Manipulation Language
√ Python-SQL Connectivity

√ Introduction to Excel
√ Pre-defined Functions, √ Datetime functions
√ String functions, √ Mathematical functions
√ Lookup
√ Logical and error εfunctions

Data science caps projects will allow you to implement the skills learned in our Data Science training program.

With Dedicated Mentoring Sessions, You’ll know how to solve a real industry-aligned problem.