Master in Python at top institutes in Uttam Nagar & Yamuna Vihar

What you'll learn

These topics will help you learn python from scratch.

Python Syntax.
Python statements, indentation and comments.
Python variable and data types.
Python operator.
Python numbers.
Python strings.
Python data structures.
Python lists.

More....

Requirements

Innovative and Creative Ideas
Basic Knowledge of Computer

What placement assistance will you receive?

Free Placement Preparation Training

Access to curated Internships & Current Job Openings.

Top performers will be highlighted on Attitude Job portal

Requirements

Python For Data Analytics are amongst the most fundamental ingredients in the recipe for creating efficient algorithms and good software design. Knowledge of how to create and design good data structures is an essential skill required in becoming an exemplary programmer. This course will teach you how to master the fundamental ideas surrounding Python For Data Analytics.

Course Circullum

Introduction to Data Analytics with Python

Understanding the role of data analytics in decision-making
Introduction to Python and its data analytics libraries (e.g., NumPy, Pandas, Matplotlib)

Python Fundamentals for Data Analysis

Basics of Python programming (variables, data types, loops, and functions)
Data structures in Python (lists, dictionaries, tuples)
File handling (reading and writing data)

Data Wrangling with Pandas

Introduction to Pandas for data manipulation
Reading and writing data with Pandas
Data cleaning and preprocessing techniques
Data merging and aggregation

Data Visualization with Matplotlib and Seaborn

Data visualization fundamentals
Creating static and interactive plots
Customizing charts and graphs
Exploratory data analysis (EDA)

Numerical Computing with NumPy

Introduction to NumPy arrays
Performing mathematical and statistical operations
Handling missing data

Working with Time Series Data

Handling time series data with Pandas
Time-based indexing and resampling
Time series data visualization

Statistical Analysis with Python

Descriptive statistics and data summarization
Inferential statistics and hypothesis testing
Correlation and regression analysis

Machine Learning with Scikit-Learn (Introduction)

Overview of machine learning
Supervised and unsupervised learning
Data preprocessing for machine learning
Building and evaluating machine learning models

Data Sources and Data Acquisition

Data sources and APIs for data acquisition
Web scraping and data retrieval
Data integration and transformation

Data Ethics and Privacy

Ethical considerations in data analytics
Data privacy and GDPR compliance
Responsible AI and ethical AI practices

Data Analytics Case Studies

Practical data analytics case studies in various domains (e.g., finance, healthcare, marketing)
Analyzing real-world datasets
Project-based learning

Final Data Analytics Project

Students work on a data analytics project from start to finish
Project proposal, data analysis, visualization, and presentation
Building a portfolio of data analytics projects

Course Conclusion and Next Steps

Review of key takeaways and skills acquired
Opportunities for further learning and career prospects in data analytics
Preparing for certifications and advanced data analytics topics

Take the final exam online to complete the Python For Data Analytics after which you will be able to download your certificate from Attitude Trainings.