Data Science Course
Data Analysis and Machine Learning to Extract Insights From the Data.
Languages and Tools covered
Online (Discount Price)
₹71,500/ ₹50000/*
(Inc 30% Discount)
- Online Training Program
Offline (Discount Price)
₹78,500/ ₹55000/*
(Inc 30% Discount)
- Classroom Training Program
- 20 Weeks, 60 Sessions Program
- 4 Project
- Hybrid Learning Model
- Certification Program
- Internship Included
- Job Placement Assistance
- Hackathon with Certification
- Regular or Weekend Batches
Get FREE 1:1 Counselling
Python with Libraries
This module covers the essential fundamentals of Python programming. Starting from the basics of Python syntax and progressing through key concepts such as sequences, conditional statements, and control loops. Students will gain a solid foundation in Python, enabling them to write and understand simple to moderately complex programs.
Introduction to Python
+
- Programming Basics using Python
- Scientific & Numerical Computing
- Algorithm Thinking, and Flow Charting
- Python IDE, Jupyter Notebook, VS Code
- Expressions, Indentation, Keywords, Identifiers
- Variables, Comments, Type Casting, Data Types
- input() & print(), Types of Operators
Advanced Python Concepts
+
- Random Module, Zip, Functions (Default & User-Defined)
- Parameters vs. Arguments, Lambda, map, reduce, filter
- Namespaces, Scope of Variables
- OOP Concepts, Inheritance, Polymorphism, Multithreading
- Exception Handling, Try & Except, finally & raise
- NumPy Library, Pandas Library
Visualization in Python
+
- Basics of Data Visualization
- Matplotlib for Data Storytelling: Different charts/plots
- Seaborn for Data Storytelling: Different charts, Styling
Business Statistics for Data Science
This module introduces the foundational concepts of business statistics, equipping students with the skills needed to analyze and interpret data for informed business decision-making. Students will gain a comprehensive understanding of both descriptive and inferential statistics and their applications in business contexts.
Descriptive Statistics
+
- Data vs Information vs Insight
- Introduction to Business Statistics
- Data Representation
- Measures of Central Tendency (Mean, Median, Mode)
- Measures of Distribution
- Normal Distribution
- Measures of Dispersion (Variance, SD)
- Measures of Position (Deciles, Percentiles, Quartiles, etc.)
- Standardization, Covariance, Correlation, Regression
Inferential Statistics
+
- Introduction to Probability
- Measuring Probability
- Types of Probability
- Law of Large Numbers
- Bayes Theorem
- Sampling Techniques
- Central Limit Theorem
- Hypothesis Testing, Confidence Interval, p-value, Significance Level
- Type I & II Errors
- t-test, z-test, Chi-Square, ANOVA
Exploratory Data Analysis (EDA)
+
- Data Loading from Multiple Sources
- Data Analysis, Management, Aggregation, Correlation Analysis
- Date & Time Methods, Analyzing Real-time Data
- Applying Visualization, Insight Generation, and Reporting
Feature Engineering
+
- Understanding Features, Feature Selection
- Creating, Extraction, Encoding, Scaling, and Transformation Techniques
- Data Pre-processing for Analysis & ML
Advanced Excel for Analytics
This module delves into the advanced features and capabilities of Excel, empowering students to perform sophisticated data analysis and make data-driven decisions. Students will learn how to manage and analyze data efficiently using advanced Excel functions and tools.
Advanced Excel
+
- Getting Started with Excel, Introduction, Toolbar
- Formatting in Excel, Formula Tab, Essential Formulae
- Functions in Excel, Errors & Handling
- Conditional Statements, String Handling, Statistical Functions
- Date & Time Functions, LOOKUP in Excel
- Data Toolbar, Advanced Analytics Tool Pack
- Crosstab Analysis & Pivoting
- Macros in Excel, Editing & Referencing, Data Protection
- Visualization using Excel
MIS Reporting
+
- Basics of Management Information Systems
- MIS Techniques, Tools for MIS
- Creating Reports and Dashboards using Excel
- Auto-referencing, Updates, Publishing Reports
Database Concepts & MySQL
This module provides a comprehensive introduction to relational databases, which are fundamental to organizing and managing data in many modern applications. Students will learn the principles of relational database design, querying, and management, enabling them to store, retrieve, and manipulate data efficiently.
Relational Databases
+
- Introduction to Data & Databases
- Understanding Relational Databases
- Data Storing, DBMS, Schema
- Queries, Indexes, and Security
- Types of Databases
SQL
+
- Introduction to SQL, Data Structure, Basic Commands
- Data Querying, Constraints, Functions, Manipulation
- Data Definition, Installing MySQL, Setting up MySQL Server
- DDL, DML, DQL, TCL, DCL
- Joins, Union, Union All, Views, Aggregating Data
- Sub Queries, Window Functions
- Stored Procedures, Exception Handling, Triggers, Common Table Expression
Power BI
This module provides an in-depth exploration of Power BI, a powerful business analytics tool. Students will learn how to use Power BI to transform raw data into meaningful insights through interactive dashboards and reports.
Exploring Power BI
+
- Introduction, Installation, Parts: Desktop, Service, Mobile apps
- Power BI Workflow, Importing Data: Loading data from different sources
- Power Query Editor, Data source connectivity
Advanced Power BI
+
- Data Cleaning and Transformation, Data Shaping
- Query Dependencies, Parameters, M Language
- Data Transformations: Cleaning, Filtering, Sorting, Aggregating, Reshaping, Joining, Splitting
- Deriving Calculated Fields, Handling Time Series, Modeling, Relations between tables
- Analytical Queries with DAX: Columns, Measures, Aggregation, Math, Logical, Relationship, Filter
- Information, Table Manipulation, Text DAX Functions, DAX Query Tools
- Time Intelligence: Functions, Reports with Time Intelligence
- Visualizations with Data Points, Buttons, Bookmark, Drill-through, Applying Filters
- Publishing Reports into Service, Multi-page, Simple to Complex
- Row Level Security
Tableau for Business Intelligence and Data Analytics
This course offers a comprehensive introduction to Tableau, a powerful tool for business intelligence and data visualization. Students will learn to effectively load, transform, and model data within Tableau, enabling them to create insightful and interactive visualizations.
Foundations of Tableau
+
- Installation, Tableau vs. Power BI
- Components, MDX Functions, Parameters, Calculated Fields
- Geographic & Time Series Visualizations
- Dashboard and Stories, Publishing
Machine Learning
This course provides a foundational overview of machine learning, covering key concepts, techniques, and applications. Students will learn about different types of machine learning, including supervised, unsupervised, and reinforcement learning.
Essentials of Machine Learning
+
- Exploring ML, Supervised vs. Unsupervised
- Data Pre-processing vs. Accuracy, Classification vs. Clustering
- Architecture of ML, Training & Testing Data, Hyperplane
Linear Regression
+
- Simple, Multiple & Polynomials, Lasso
- Model Training & Evaluation Metrics
- Understanding Errors (SSE, SSR, SST, MSE)
- R-Square, Adj. R Square, RMSE, OLS Method, Cost Function, Gradient Descent
Classification - Logistic Regression
+
- Logistic Regression, Log Odds, Model Optimization
- Confusion Matrix, Bias & Variance
Machine Learning – Supervised
This module provides an in-depth exploration of supervised learning, a fundamental aspect of machine learning where models are trained using labeled data. Students will learn key concepts, algorithms, and techniques used in supervised learning to build predictive models and make data-driven decisions.
Advanced Supervised ML Models
+
- Decision Trees, Impurity, Entropy, IG, Gini Index
- Ensemble Learning: Random Forest, Bagging & Boosting
- K-NN Algorithm, Naïve Bayes Algorithm
- Text Classification using NB, Support Vector Machines (SVM), and Kernel Trick
- K-fold Cross-Validation, Grid Search CV, Imbalanced Data Set
Unsupervised ML Models
+
- Clustering Algorithms: K-means, Optimal K-value methods
- Exclusive, Overlapping & Hierarchical Clustering (Agglomerative, Divisive)
- Dendrograms, Understanding Fuzzy C-means
- Dimensionality Reduction, PCA, Modeling PCA
- Association Rule Learning, Market Basket Analysis
Artificial Intelligence / Deep Learning
This module provides a comprehensive introduction to Artificial Intelligence (AI) and Deep Learning, two cutting-edge fields in technology. Students will gain a solid understanding of AI principles and deep learning techniques, focusing on neural networks and their applications in solving complex problems.
Introduction to Neural Networks
+
- Introduction, Perceptron, AI Perspective, Neural Networks
- Layers & Weights, ANN, Optimization Algorithm
- Understanding Back Propagation, Loss Function
- Gradient Descent, Model Evaluation in ANN
Deep Learning
+
- Understanding Deep Learning, Activation Functions
- Diving into CNN, Deep Convolution
- Basics of Computer Vision & Applications
- Understanding RNN, LSTM, Bi-directional LSTM
- Introduction to NLP, Time Series Analysis
Additional Concepts
This module covers various advanced topics in data science and AI, including cloud computing, generative AI, and time series analysis. Students will explore cutting-edge technologies and apply their skills in real-world projects.
Cloud Computing
+
- Introduction to Cloud Computing, AWS vs. Azure
- Understanding Platforms, Deploying an Application over Cloud
- AI Tools in Cloud Computing
Generative AI
+
- Introduction to Generative AI, Types of AI
- Chatbots, ChatGPT, Architecture of ChatGPT
- Building a Chatbot, Using Prompt Engineering in DS/AI
Time Series Analysis
+
- Understanding Time Series, Metrics & Events, Analysis
- Auto Regression (AR), Auto Correlation, Moving Average Method
- Stationarity of Time Series, ARIMA, and ML for Time Series
Real-time Projects
Curriculum
The Curriculum supports step-by-step transformation while building confidence to handle various domain problems.
60 Sessions
Learning content
15+
Languages & Tools
WhyData Science
Data Science is both an art and a science, combining data extraction, analysis, and application to create models that inform business decisions. It is relevant across all domains and is increasingly in demand as businesses seek predictive models to drive decision-making.
- Art & Science:
- Data Science is a unique blend of art and science. It involves extracting and analyzing relevant data from various sources to solve business problems.
- Core Techniques:
- Data Science combines Data Analytics and Machine Learning techniques to develop decision-making models essential for business decisions.
- Wide Applicability:
- Data Science is applicable and widely used across all business domains. With increasing data volume and complexity, the demand for skilled data scientists continues to grow.
Project Base Learning
MASTER FROM DATA SOURCING TO MODEL BUILDING
Your Pathway to Mastery
Data Science Learning Path
A comprehensive roadmap to becoming job-ready in Data Science.
- Python Pre-Videos
- Learn before attending sessions.
- Offline / Online Session
- Choose your favorite mode of learning.
- Recorded Sessions
- In case you missed sessions or need a re-run.
- Jupyter Notebooks
- Download code and notes as needed.
- Assignments
- Convert theoretical knowledge into skills.
- Periodical Assessments
- Self-test to identify gaps & seek support.
- Projects - Mini & Major
- Showcase your skills through project-based learning.
- Interview Preparation
- 100s of questions & answers for practice.
- Final Assessment
- Thorough assessment to identify gaps.
- Job Readiness
- Mock interviews, CV prep, projects, HR / expect rounds, and more.
Course Information
This Data Science course emphasises on Project-Based Learning to to meet the learning needs of students from various background and make them job-ready. Learn Data Science like a pro and our methodology invoke thought process in the learner to solve problems. Post completion of the course learners could independently build a Data Science solution using Machine Learning models. You would be offered a chance to secure an internship with relevant industries and participate in our hackathons.
All-in-one Data Science Program
Our Data Science course is designed to prepare you for the industry with a comprehensive curriculum that emphasizes practical knowledge and skills. Through a project-based approach and industry-driven courses, you'll gain the expertise needed to excel in the field of Data Science.
- Industry-Ready Program
- Our curriculum is tailored to meet industry standards, ensuring that you are job-ready by the time you complete the program.
- Project Based Approach
- Learn by doing with our hands-on projects that allow you to apply theoretical knowledge to real-world scenarios.
- Industry Driven Courses
- Our courses are designed in collaboration with industry experts to ensure they align with current market needs.
- Hybrid Learning Model
- Experience a blend of online and offline learning for flexible and effective education.
- Minimum Batch Size
- Our small batch sizes ensure personalized attention and better learning outcomes.
- Job Preparation with Profile building
- Prepare for your dream job with expert guidance on resume building, portfolio creation, and mock interviews.
- Internships and projects
- Gain practical experience through internships and projects, giving you a competitive edge in the job market.
- Scholarship Assessments
- Take advantage of scholarship opportunities to help finance your education and reduce course costs.
- Knowledge-Skill Pedagogy
- Our teaching approach focuses on bridging the gap between knowledge and practical skills, ensuring you can apply what you learn in real-world situations.
Course Highlights
If you're preparing to enhance your skills and knowledge. Our courses emphasize foundational learning, practical implementation through projects, and real-world experience, ensuring you are well-prepared for the challenges of your chosen field. From engaging hybrid learning sessions to hands-on internships, our comprehensive approach is designed to build your confidence and transform your expertise into career-ready skills.
Prepare
Foundation courses designed to bring you up to the speed, irrespective of your background.
Learn
Hybrid Learning to engage learners even before the class for effective knowledge transfer.
Implement
Project-Based Learning for confidence building and transferring knowledge into skills.
Experience
Internships & Job Readiness to give real-time experience and prepare for interviews.
Prospects as a Data Science
We have understood by now that Python is a popular and versatile high-level programming language that is highly recommended for those who wish to step into the world of Programming. There is a demand for professionals with Python programming skills to be developers or work with the Data Science team. However, it is always recommended to pick up a track towards being a Python application developer or DS /AI. This would offer a career continuation and assure you to find the right career opportunities
Contact us
Get in Touch With Us
- Phone number
- 8977944952/53
- hello@cedlearn.com
- DELHI
HYDERABAD
KOCHI
BANGLORE
Business Hours :
Monday - Saturday: 9:00a.m to 8:00p.m
Sunday: With Appointment Only
Send a Message
Write to us. We would catch up with you as soon as we receive your message
scholarship & discounts
We believe in encouraging talented learners with Scholarships to avoid the learning hurdles due to financial limitations. However scholarships must be awarded based on the merit and hence our assessments. We would be glad to assess learners using our multi-dimensional assessment to identify the dormant potential and thus encourage them through the learning process by offering financial benefits. To know more you could contact us or register for the online assessment. If you are not clear on the career path in the field of AI, please feel free to meet our career counsellor. We would be glad to guide you through the career options to draft a personalized career path.