Advanced Data Science and Machine Learning Masterclass
This course is tailored to make you a highly skilled Data Scientist with extensive knowledge of Python, Python Libraries, MySQL, R, ML algorithms and PowerBI.
Job Readiness | Career Guidance and Support | Industry Certifications | Flexible Learning Schedule
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Our Placements
Our Students Who Have landed Their Dream job In
Sessions
65 Lectures
Duration
130 Hrs
Placement
100% Assurance*
Job CTC
Upto 8 LPA*
Sessions
65 Lectures
Duration
130 Hrs
Placement
100% Assurance*
Job CTC
Upto 8 LPA*
LAND YOUR DREAM JOB
Ascend to Chief Data Scientist at data-centric organizations like LinkedIn
Design as a Machine Learning Architect at AI-driven companies like Nvidia
Strategize as an AI Strategy Consultant with global tech consultancies like Accenture
Lead as Director of Data Engineering at cloud services leaders like AWS
Make You Industry-Ready
EXCLUSIVE CAREER
Why Join Us?
Hands-On Training
Experience our interactive, hands-on teaching approach through a free course demo.
Industry-Leading Mentors
Learn from seasoned professionals who have pioneered advancements in their industries.
Job Readiness
Receive personalized career guidance and placement assistance.
Tools and technologies
Expert-Designed Course Structure
Hands-on training with real-world projects.
Gain practical experience by working on industry-relevant projects under expert guidance.
Training From Industry Leading Mentors
Learn from seasoned professionals who have pioneered advancements in their respective fields.
Career guidance and placement assistance.
Receive personalized support to sharpen your job search skills and secure rewarding opportunities.
Flexible learning option
Choose from online or on-premise training modes to suit your preferences and lifestyle.
1:1 Live Sessions
Live one-on-one training assistants via video call, chat and on-site with problem and solution guidance.
Comprehensive Curriculum
Master a wide range of concepts and techniques through a meticulously designed and up-to-date curriculum.
OUR CURRICULUM
Our Interactive Course Content
Topics covered
Python Programming
1. Introduction to Python Programming
- What Is Python?
- Benefits of Learning Python Programming Compared to Other Programming Languages?
- Interpreted vs compiled
- Dynamic programming
- Scripted vs GUI vs Interactive mode
2. Installation & Environment Settings of Python IDE
- Installation of Anaconda
- Installation of PyCharm
- Installation of VS Code
- How to use VS Code, Jupyter & PyCharm.
- How to use google Collab and it’s benefits
3. Python Basics
- Variables
- Datatypes
- Operators
4. Python Numbers
- Integer
- Float
- complex
5. Python String
- String Slicing
- Iterating Over String
6. Python Container Objects
- List
- Tuple
- Dictionary
- Sets
7. Conditions & Looping
- If Condition
- EIif Condition
- Else Condition
- While Loop
- For Loop
- Break & Continue Statement
8. Unpacking
- List Unpacking
- Tuple Unpacking
- Dictionary Unpacking
9. Comprehensions
- List Comprehension
- Dictionary Comprehension
10. Functions
- Basic Functions
- Lambda Functions/Expression
- Map() Function/Expression
11. Python Programming
- Palindrome, Fibonacci, factorial
- Prime number, divisibility test, count of substring
12. Object Oriented Programming (Classes & Objects)
- Inheritance
- Encapsulation
- Polymorphism
- Abstraction
Python Libraries for ML
1. NUMPY
- Introduction to NumPy
- NumPy Array
- Array Attributes
- Array Methods
2. PANDAS
- Introduction to Pandas
- Pandas Series
- Accessing Series Elements
- Pandas Data frame – Introduction
- Data frame Creation
- Reading Data from Various Files
- Accessing Data frame
- Data frame Sorting
- Data frame Concatenation
- Data frame Joins
- Data frame Merge
- Reshaping Data frame
- Data frame Operations
- Data frame methods - head(),tail, dType, shape, get_dummies
- Checking Duplicates
- Dropping Rows and Columns
- Replacing Values
- Missing Value Analysis & Treatment
3. VISUALIZATION USING MATPLOTLIB
- Plot Styles & Settings
- Line Plot
- Multiline Plot
- Matplotlib
- Subplots
- Histogram
- Boxplot
- Pie Chart
- Scatter Plot
4. VISUALIZATION USING SEABORN
- Strip plot
- Distribution plot
- Joint plot
- Violin plot
- Swarm plot
- Pair plot
- Count plot
- Heatmap
Machine Learning Algorithms
1. ML Fundamentals
- What is DS/ AI/ Ml with examples
- ML Types
- Algorithm vs model
- Using Google colab
2. Supervised Learning
- Linear Regression with ordinary Least Square (OLS)
- Optimization Technique
- Linear Regression with Stochastic Gradient Descent (SGD)
- Logistic Regression
- Decision Trees
- Random Forests & Gradient Boosting
- Ensemble Techniques
- Support Vector Machines (SVM)
- Naïve Bayes
- KNN algorithm
3. Unsupervised Learning
- Principal Components Analysis (PCA)
- K-Means Clustering
- Implementation of K-Means
- Hierarchical Clustering
- Types of Hierarchical Clustering
- DBSCAN
MySQL
1. Introduction to DBMS
2. What is MySQL?
3. Installation of MySQL
4. Overview of MySQL Workbench
5. Different Clauses in MySQL
- The SELECT clause
- The WHERE clause
- The AND, OR and NOT operators
- The IN operator
- The BETWEEN operator
- The LIKE operator
- The REGEXP operator
- The IS NULL operator
- The ORDER BY clause
- The LIMIT clause
- The GROUP BY clause
- The HAVING clause
6. Operators in MySQL
- Arithmetic operators
- Concatenation operators
- Comparison operators
- Relational operator
- Logical operator
- Special operator
7. Types of Joins
- INNER JOINS
- JOINING ACROSS DATABASES
- SELF JOINS
- JOINING MULTIPLE TABLES
- COMPOUND JOIN CONDITIONS
- IMPLICIT JOIN SYNTAX
- OUTER JOINS
- OUTER JOIN BETWEEN MULTIPLE TABLES
- SELF-OUTER JOINS
- THE USING CLAUSE IN JOINS
- NATURAL JOINS
- CROSS JOINS
- UNIONS
8. SUB-QUERY
9. Data Definition Language (DDL)
- Create
- Rename
- Alter
- Truncate
- Drop
10. Data Manipulation Language (DML)
- Insert
- Update
- Delete
11. Transaction Control Language (TCL)
- Commit
- Rollback
- Savepoint
12. Data Control Language (DCL)
- Grant
- Revoke
Data Visualization, Statistics & ML using R programming
1. Introduction to R
- Introduction to RStudio's,
- RObjects - vectors, list, factors, matrix, arrays and data frames
2. Data Visualization
- Without using library
- Using GGplot2 library
3. Statistics in R
- Mean/Median/Mode
- 1 2 3 Quartile
- Reading csv and excel file
4. Implementing ML in R
- R project 1 using lm() (linear regression)
- R project 2 using glm() (logistic regression)
PowerBI
1. Transform data using power query editor
- M- code and applied steps
- How to load data from csv. excel
- Load data from folder
- Conditional columns
- Transform vs add column
2. Creating relationship model
- What are power131 relationship
- Model View
- Star vs snowflake model
- bi- directional filters
3. DAX
- calculated columns vs measure
- Implicit vs explicit measure
- SUMXO
- Calculate()
- ALL()
- Date time functions
4. Creating interactive Dashboard
- Matrix
- KPI
- Line chart
- Forecasting
- Gauge
- Maps
Land your Dream Jobs
In Companies Like
Experience the CDPL
Training Approach
Video Courses | Bootcamps | CDPL | |
---|---|---|---|
Real work experience | ✖ | ✖ | ✔ |
True, project-based learning | ✖ | ✖ | ✔ |
Live sessions & mentorship | ✖ | ✔ | ✔ |
Job-ready portfolio | ✖ | ✖ | ✔ |
Externship with top companies | ✖ | ✖ | ✔ |
Career guidance | ✖ | ✔ | ✔ |
Placement Assurance | ✖ | ✖ | ✔ |
Eligibility
Undergraduates
This course is structured for any undergraduate or job seeker who wants to start his career in Data Science & Machine Learning field.
Graduates
Any Fresh graduate or post-graduate looking to secure a career in the IT domain.
Professionals
Any working professional with experience in the non-IT domain and looking to enter the IT field.
Our Process
LIVE Learning
Experience Immersive Learning Through Our Live Classrooms
Onboarding Session
Kick-start Your Learning Journey with Our On-boarding Session
Certification & Placement Support
Certification to Career: Let Us Guide Your Path to Success
- Get free demo session
- Online Sessions
- Hands on session
- Placement Assurance*
Some figures that matters
Learners
Years of Industry Experience
Corporate Clients