DATA ANALYSIS WITH PYTHONExplore the world of data analysis with Python! Learn to dissect data using multi-dimensional arrays in numpy, harness the power of Data Frames in pandas, leverage the SciPy library for mathematical routines, and delve into machine learning with scikit-learn. This course provides hands-on experience through labs using real Python tools like Jupyter notebook in JupyterLab, making your journey into data analytics both insightful and practical. MODULE 1 Introduction Datatypes, Variables, and Operators String and its Associated Functions If-Else Statement MODULE 2 Regular Expressions Looping Statements Nested Loop Pattern-Based Problem MODULE 3 Data Structure in – Python: List Tuple Dictionary Functions in Python: Recursion Functions Return Statement Modules in Python MODULE 4 List Comprehension File Handling in Python: Reading, Writing, and Appending into Files Comparing Files Classes and Objects Inheritance and Polymorphism MODULE 5 Numpy for Creating Arrays Data Analysis Using Pandas Matplotlib for Graphical Representation DATA ANALYSIS WITH PYTHONCOURSE SYLLABUS MODULE 1 IMPORTING DATASETS Learning Objectives Understanding the Domain Understanding the Dataset Python package for data science Importing and Exporting Data in Python Basic Insights from Datasets MODULE 2: CLEANING AND PREPARING THE DATA Identify and Handle Missing Values Data Formatting Data Normalization MODULE 3: SUMMARIZING THE DATA FRAME Descriptive Statistics Basics of Grouping ANOVA Correlation More on Correlation MODULE 4: MODEL DEVELOPMENT Simple and Multiple Linear Regression Model Evaluation Using Visualization Polynomial Regression and Pipelines R-squared MSE for In-Sample Evaluation Prediction and Decision Making MODULE 5: MODEL EVALUATION Model Evaluation Over-fitting Under-fitting Model Selection Ridge Regression Grid Search D A T A V I S U A L I Z A T I O N W I T H P Y T H O NCOURSE SYLLABUS MODULE 1: INTRODUCTION TO VISUALIZATION TOOLS** Introduction to Data Visualization Introduction to Matplotlib Basic Plotting with Matplotlib Line Plots MODULE 2: BASIC VISUALIZATION TOOLS Area Plots Histograms Bar Charts MODULE 3: SPECIALIZED VISUALIZATION TOOLS Pie Charts Box Plots Scatter Plots Bubble Plots MODULE 4: ADVANCED VISUALIZATION TOOLS Waffle Charts Word Clouds Seaborn and Regression Plots

DATA ANALYSIS WITH PYTHON Explore the world of data analysis with Python! Learn to dissect data using multi-dimensional arrays in numpy, harness the power of Data Frames in pandas, leverage the SciPy library for mathematical routines, and delve into machine learning with scikit-learn. This course provides hands-on experience through labs using real Python tools like Jupyter notebook in JupyterLab, making your journey into data analytics both insightful and practical. MODULE 1 Introduction Datatypes, Variables, and Operators String and its Associated Functions If-Else Statement MODULE 2 Regular Expressions Looping Statements Nested Loop Pattern-Based Problem MODULE 3 Data Structure in – Python: List Tuple Dictionary Functions in Python: Recursion Functions Return Statement Modules in Python MODULE 4 List Comprehension File Handling in Python: Reading, Writing, and Appending into Files Comparing Files Classes and Objects Inheritance and Polymorphism MODULE 5 Numpy for Creating Arrays Data Analysis Using Pandas Matplotlib for Graphical Representation DATA ANALYSIS WITH PYTHON COURSE SYLLABUS MODULE 1 IMPORTING DATASETS Learning Objectives Understanding the Domain Understanding the Dataset Python package for data science Importing and Exporting Data in Python Basic Insights from Datasets MODULE 2: CLEANING AND PREPARING THE DATA Identify and Handle Missing Values Data Formatting Data Normalization MODULE 3: SUMMARIZING THE DATA FRAME Descriptive Statistics Basics of Grouping ANOVA Correlation More on Correlation MODULE 4: MODEL DEVELOPMENT Simple and Multiple Linear Regression Model Evaluation Using Visualization Polynomial Regression and Pipelines R-squared MSE for In-Sample Evaluation Prediction and Decision Making MODULE 5: MODEL EVALUATION Model Evaluation Over-fitting Under-fitting Model Selection Ridge Regression Grid Search D A T A V I S U A L I Z A T I O N W I T H P Y T H O N COURSE SYLLABUS MODULE 1: INTRODUCTION TO VISUALIZATION TOOLS** Introduction to Data Visualization Introduction to Matplotlib Basic Plotting with Matplotlib Line Plots MODULE 2: BASIC VISUALIZATION TOOLS Area Plots Histograms Bar Charts MODULE 3: SPECIALIZED VISUALIZATION TOOLS Pie Charts Box Plots Scatter Plots Bubble Plots MODULE 4: ADVANCED VISUALIZATION TOOLS Waffle Charts Word Clouds Seaborn and Regression Plots

Certificate in Python Programming

Instructor-Led / Self-Paced Training
Real Life Case Discussions
Detailed Material for Practice
Quiz & Assignments
Certificate Of Completion
World Class Seasoned Faculty

CFI – India’s Leading Finance and Analytics Training Institute

CFI’s Program on “Certificate in Python Programming” prepares you to become a Certified Python Programming Expert. The program equips candidates with the skills and insights to thrive in today’s data-driven business landscape. Python Course is designed to empower professionals and aspiring analysts with the knowledge and expertise required to extract actionable insights, drive informed decision-making, and propel organizational success.

Whether you are an aspiring Data Analyst eager to enter the field or a seasoned professional looking to upskill, this course is your gateway to mastering the symbiotic relationship between business strategy and data analytics.

Top Hiring Companies

Who Should Enroll?

Our training is open to ambitious candidates who come from a range of backgrounds and will typically be

Speak to our Counselor to know if you are good fit for the program at +91-9354266554 or Mail Us at [email protected]

Course Format

Duration:

40 hours of training
spread over 4 Weeks

Format:

Blended learning with a mix of live lectures, hands-on workshops, and real-world case studies

Assessments:

Practical assignments, quizzes, and a final project applying learned concepts to a business scenario.

Technologies / Covered

Key Areas of Learning

Business Strategy Integration

Data Exploration and Visualization

Statistical Analysis for Decision-Making

Data-Driven Decision-Making

Python Programming - Program Curriculum

Python – 48 hrs

DATA ANALYSIS WITH PYTHON
Explore the world of data analysis with Python! Learn to dissect data using multi-dimensional arrays in numpy, harness the power of Data Frames in pandas, leverage the SciPy library for mathematical routines, and delve into machine learning with scikit-learn. This course provides hands-on experience through labs using real Python tools like Jupyter notebook in JupyterLab, making your journey into data analytics both insightful and practical.

MODULE 1
- Introduction
- Datatypes, Variables, and Operators
- String and its Associated Functions
- If-Else Statement
MODULE 2
- Regular Expressions
- Looping Statements
- Nested Loop
- Pattern-Based Problem
MODULE 3
- Data Structure in – Python:
- List
- Tuple
- Dictionary
- Functions in Python:
- Recursion
- Functions Return Statement
- Modules in Python
MODULE 4
- List Comprehension
- File Handling in Python:
  - Reading, Writing, and Appending into Files
  - Comparing Files
- Classes and Objects
- Inheritance and Polymorphism
MODULE 5
- Numpy for Creating Arrays
- Data Analysis Using Pandas
- Matplotlib for Graphical Representation

DATA ANALYSIS WITH PYTHON
COURSE SYLLABUS

MODULE 1
- IMPORTING DATASETS
- Learning Objectives
- Understanding the Domain
- Understanding the Dataset
- Python package for data science
- Importing and Exporting Data in Python
- Basic Insights from Datasets
MODULE 2: CLEANING AND PREPARING THE DATA
- Identify and Handle Missing Values
- Data Formatting
- Data Normalization
MODULE 3: SUMMARIZING THE DATA FRAME
- Descriptive Statistics
- Basics of Grouping
- ANOVA
- Correlation
- More on Correlation
MODULE 4: MODEL DEVELOPMENT
- Simple and Multiple Linear Regression Model
- Evaluation Using Visualization
- Polynomial Regression and Pipelines
- R-squared
- MSE for In-Sample Evaluation
- Prediction and Decision Making
MODULE 5: MODEL EVALUATION
- Model Evaluation
- Over-fitting
- Under-fitting
- Model Selection
- Ridge Regression
- Grid Search

D A T A V I S U A L I Z A T I O N W I T H P Y T H O N
COURSE SYLLABUS

MODULE 1: INTRODUCTION TO VISUALIZATION TOOLS**
- Introduction to Data Visualization
- Introduction to Matplotlib
- Basic Plotting with Matplotlib Line Plots
MODULE 2: BASIC VISUALIZATION TOOLS
- Area Plots
- Histograms
- Bar Charts
MODULE 3: SPECIALIZED VISUALIZATION TOOLS
- Pie Charts
- Box Plots
- Scatter Plots
- Bubble Plots
MODULE 4: ADVANCED VISUALIZATION TOOLS
- Waffle Charts
- Word Clouds
- Seaborn and Regression Plots

Python – 48 hrs

DATA ANALYSIS WITH PYTHON Explore the world of data analysis with Python! Learn to dissect data using multi-dimensional arrays in numpy, harness the power of Data Frames in pandas, leverage the SciPy library for mathematical routines, and delve into machine learning with scikit-learn. This course provides hands-on experience through labs using real Python tools like Jupyter notebook in JupyterLab, making your journey into data analytics both insightful and practical.

MODULE 1
- Introduction
- Datatypes, Variables, and Operators
- String and its Associated Functions
- If-Else Statement
MODULE 2
- Regular Expressions
- Looping Statements
- Nested Loop
- Pattern-Based Problem
MODULE 3
- Data Structure in – Python:
- List
- Tuple
- Dictionary
- Functions in Python:
- Recursion
- Functions Return Statement
- Modules in Python
MODULE 4
- List Comprehension
- File Handling in Python:
  - Reading, Writing, and Appending into Files
  - Comparing Files
- Classes and Objects
- Inheritance and Polymorphism
MODULE 5
- Numpy for Creating Arrays
- Data Analysis Using Pandas
- Matplotlib for Graphical Representation

DATA ANALYSIS WITH PYTHON COURSE SYLLABUS

MODULE 1
- IMPORTING DATASETS
- Learning Objectives
- Understanding the Domain
- Understanding the Dataset
- Python package for data science
- Importing and Exporting Data in Python
- Basic Insights from Datasets
MODULE 2: CLEANING AND PREPARING THE DATA
- Identify and Handle Missing Values
- Data Formatting
- Data Normalization
MODULE 3: SUMMARIZING THE DATA FRAME
- Descriptive Statistics
- Basics of Grouping
- ANOVA
- Correlation
- More on Correlation
MODULE 4: MODEL DEVELOPMENT
- Simple and Multiple Linear Regression Model
- Evaluation Using Visualization
- Polynomial Regression and Pipelines
- R-squared
- MSE for In-Sample Evaluation
- Prediction and Decision Making
MODULE 5: MODEL EVALUATION
- Model Evaluation
- Over-fitting
- Under-fitting
- Model Selection
- Ridge Regression
- Grid Search

D A T A V I S U A L I Z A T I O N W I T H P Y T H O N COURSE SYLLABUS

MODULE 1: INTRODUCTION TO VISUALIZATION TOOLS**
- Introduction to Data Visualization
- Introduction to Matplotlib
- Basic Plotting with Matplotlib Line Plots
MODULE 2: BASIC VISUALIZATION TOOLS
- Area Plots
- Histograms
- Bar Charts
MODULE 3: SPECIALIZED VISUALIZATION TOOLS
- Pie Charts
- Box Plots
- Scatter Plots
- Bubble Plots
MODULE 4: ADVANCED VISUALIZATION TOOLS
- Waffle Charts
- Word Clouds
- Seaborn and Regression Plots

Why CFI Education

Case Study Approach

Real life cases for most effective and skill based learning.

Passionate Educators

Highly experienced instructors who bring real business problems to the classroom.

Individual Attention

Focus on building technical expertise and ensure the learning of all candidates.

Capstone Projects

A month long project work post classroom sessions to gain confidence in their domain area.

Soft Skill Development

Helps candidates in understanding best practices and soft skills development for cracking interviews.

100% Placement Assistance

Have a dedicated team of placement who work towards, CV building and scheduling interviews.

Python Programming - Program Fee

	Classroom / Live Online	Self-Paced
	INR 25000	INR 15000
Classroom / Live Zoom	Yes	–
Class Recordings	Yes	Yes
Detailed Study Material	Yes	Yes
Case Studies	Yes	Yes
App Access (Android)	Yes	Yes
Live Doubts Sessions	Yes	–
Whatsapp Group	Yes	–
Email Support	Yes	–
	BUY NOW	BUY NOW

100% Placement Support*

CFI Education provides placement assistance to all the candidates who complete our in-house Financial Modeling or Investment banking programs. Apart from technical classroom training, we prepare candidates for soft skills, and conduct mock interviews. Once they feel confident, they can start applying for various job opportunities provided by our dedicated placement team. The placement process is usually start at 8th week of the training program.

We charge a placement fee of 5% of annual fixed ctc. This fee is applicable only when candidates is selected through our placement team efforts and accepts the job offer. Contact our team to know more about how CFI Education can help you launch your career in core finance domain.

After completing Python course, students will be able to pursue diverse and rewarding career paths across various industries including:

1. Data Analyst:

As a data analyst, you will play a crucial role in interpreting and analyzing data to help organizations make informed decisions. You'll work with data visualization tools, statistical techniques, and reporting frameworks.

2. Business Analyst:

Business analysts bridge the gap between business needs and technological solutions. With a strong understanding of both business strategy and data analysis, you can analyze processes, identify areas for improvement, and recommend data-driven solutions.

3. Business Intelligence Analyst:

Specializing in business intelligence, you'll focus on collecting, analyzing, and visualizing data to help businesses gain insights into their operations, customers, and market trends.

4. Data Scientist:

For those interested in a more advanced role, data science involves using machine learning and advanced statistical Techniques to extract deeper insights and predict future trends, contributing to strategic decision-making.

5. Market Research Analyst:

Leveraging your skills in data analysis, you can work as a market research analyst, helping companies understand market trends, consumer behavior, and competitive landscapes.

6. Operations Analyst:

Operations analysts use data to optimize business processes and improve efficiency. Your skills in data analysis can help organizations streamline operations and reduce costs.