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IBM
IBM
Data Science and Analytics
Data Science and Analytics
Unleash the Power of Data: Beginner to Intermediate in less than a semester.
Unleash the Power of Data: Beginner to Intermediate in less than a semester.
Unleash the Power of Data: Beginner to Intermediate in less than a semester.
DURATION
3 months
Intensive
MODE
LIVE Online
Intensive
FORMAT
Hybrid
Hands-on, Theoretic
STARTING
June, 2024
Program Details
Program Details
Program Details
Launch your future in the hottest field! Our Data Science and Analytics Pre-graduation program equips you with the in-demand skills to thrive in the exciting world of data science. Master essential programming languages (Python & R), conquer data wrangling and manipulation techniques, delve into powerful statistical analysis methods, and explore the fascinating realm of machine learning algorithms. But that's not all! You'll gain invaluable practical experience through real-world industry projects, solidifying your knowledge and propelling you towards a successful data science career. Enroll now and unlock your data science potential!
Launch your future in the hottest field! Our Data Science and Analytics Pre-graduation program equips you with the in-demand skills to thrive in the exciting world of data science. Master essential programming languages (Python & R), conquer data wrangling and manipulation techniques, delve into powerful statistical analysis methods, and explore the fascinating realm of machine learning algorithms. But that's not all! You'll gain invaluable practical experience through real-world industry projects, solidifying your knowledge and propelling you towards a successful data science career. Enroll now and unlock your data science potential!
Launch your future in the hottest field! Our Data Science and Analytics Pre-graduation program equips you with the in-demand skills to thrive in the exciting world of data science. Master essential programming languages (Python & R), conquer data wrangling and manipulation techniques, delve into powerful statistical analysis methods, and explore the fascinating realm of machine learning algorithms. But that's not all! You'll gain invaluable practical experience through real-world industry projects, solidifying your knowledge and propelling you towards a successful data science career. Enroll now and unlock your data science potential!
Who should enroll?
Who should enroll?
Who should enroll?
- You want to start your journey in the field of data science from scratch.
- You recently graduated and lack real-world experience in data science.
- You want to build practical skills to enhance your resume.
- You have a background in management or an MBA and want to leverage data for business insights.
- You want to transition into the data science field.
- You want to gain skills that are in high demand in the job market.
- You want to start your journey in the field of data science from scratch.
- You recently graduated and lack real-world experience in data science.
- You want to build practical skills to enhance your resume.
- You have a background in management or an MBA and want to leverage data for business insights.
- You want to transition into the data science field.
- You want to gain skills that are in high demand in the job market.
Join our community to learn, connect with like-minded peers, and get updates on the scholarship test
Limited Seats in the Cohort
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TEACHING PLAN (3 Month Program)
Week 1
Session 1: Data Cleaning and Manipulation in Excel
- Introduction to Excel for Data Science
- Data Cleaning Techniques
- Data Manipulation Functions
- Using Pivot Tables
- Data Validation and Conditional Formatting
Session 2: Building Interactive Dashboards and Visualizations in Tableau
- Introduction to Tableau
- Connecting to Data Sources
- Creating Basic Visualizations
- Advanced Chart Types
- Building Interactive Dashboards
Session 3: Linking Excel Data to Tableau for Dynamic Analysis
- Importing Excel Data into Tableau
- Creating Data Connections
- Real-time Data Updates
- Combining Excel and Tableau Features
- Case Studies and Practical Applications
Week 1
Session 1: Data Cleaning and Manipulation in Excel
- Introduction to Excel for Data Science
- Data Cleaning Techniques
- Data Manipulation Functions
- Using Pivot Tables
- Data Validation and Conditional Formatting
Session 2: Building Interactive Dashboards and Visualizations in Tableau
- Introduction to Tableau
- Connecting to Data Sources
- Creating Basic Visualizations
- Advanced Chart Types
- Building Interactive Dashboards
Session 3: Linking Excel Data to Tableau for Dynamic Analysis
- Importing Excel Data into Tableau
- Creating Data Connections
- Real-time Data Updates
- Combining Excel and Tableau Features
- Case Studies and Practical Applications
Week 1
Session 1: Data Cleaning and Manipulation in Excel
- Introduction to Excel for Data Science
- Data Cleaning Techniques
- Data Manipulation Functions
- Using Pivot Tables
- Data Validation and Conditional Formatting
Session 2: Building Interactive Dashboards and Visualizations in Tableau
- Introduction to Tableau
- Connecting to Data Sources
- Creating Basic Visualizations
- Advanced Chart Types
- Building Interactive Dashboards
Session 3: Linking Excel Data to Tableau for Dynamic Analysis
- Importing Excel Data into Tableau
- Creating Data Connections
- Real-time Data Updates
- Combining Excel and Tableau Features
- Case Studies and Practical Applications
Week 2
Session 4: Introduction to SQL and Relational Databases
- Overview of SQL
- Understanding Relational Databases
- Database Design Concepts
- Introduction to Tables, Rows, and Columns
Session 5: Writing Queries to Retrieve, Filter, and Aggregate Data
- Basic SQL Queries
- Filtering Data with WHERE Clause
- Using SELECT Statements
- Aggregating Data with GROUP BY
- Using HAVING Clause
Session 6: Joining Tables and Subqueries for Complex Analysis
- Introduction to Joins (INNER, OUTER, LEFT, RIGHT)
- Combining Data from Multiple Tables
- Subqueries and Nested Queries
- Advanced Query Techniques
- Practical Examples of Complex Queries
Week 2
Session 4: Introduction to SQL and Relational Databases
- Overview of SQL
- Understanding Relational Databases
- Database Design Concepts
- Introduction to Tables, Rows, and Columns
Session 5: Writing Queries to Retrieve, Filter, and Aggregate Data
- Basic SQL Queries
- Filtering Data with WHERE Clause
- Using SELECT Statements
- Aggregating Data with GROUP BY
- Using HAVING Clause
Session 6: Joining Tables and Subqueries for Complex Analysis
- Introduction to Joins (INNER, OUTER, LEFT, RIGHT)
- Combining Data from Multiple Tables
- Subqueries and Nested Queries
- Advanced Query Techniques
- Practical Examples of Complex Queries
Week 2
Session 4: Introduction to SQL and Relational Databases
- Overview of SQL
- Understanding Relational Databases
- Database Design Concepts
- Introduction to Tables, Rows, and Columns
Session 5: Writing Queries to Retrieve, Filter, and Aggregate Data
- Basic SQL Queries
- Filtering Data with WHERE Clause
- Using SELECT Statements
- Aggregating Data with GROUP BY
- Using HAVING Clause
Session 6: Joining Tables and Subqueries for Complex Analysis
- Introduction to Joins (INNER, OUTER, LEFT, RIGHT)
- Combining Data from Multiple Tables
- Subqueries and Nested Queries
- Advanced Query Techniques
- Practical Examples of Complex Queries
Week 3
Session 7: Programming Fundamentals
- Variables and Data Types
- Control Flow (if statements, loops)
- Basic Input and Output
- Writing and Running Simple Programs
Session 8: Working with Lists, Dictionaries, and Functions
- Lists and List Operations
- Dictionaries and Dictionary Operations
- Defining and Calling Functions
- Function Parameters and Return Values
- Scope and Lifetime of Variables
Session 9: Introduction to NumPy and Pandas for Data Manipulation
- Overview of NumPy
- NumPy Arrays and Operations
- Overview of Pandas
- DataFrames and Series
- Data Manipulation with Pandas (selecting, filtering, merging)
- Basic Data Cleaning Techniques
Week 3
Session 7: Programming Fundamentals
- Variables and Data Types
- Control Flow (if statements, loops)
- Basic Input and Output
- Writing and Running Simple Programs
Session 8: Working with Lists, Dictionaries, and Functions
- Lists and List Operations
- Dictionaries and Dictionary Operations
- Defining and Calling Functions
- Function Parameters and Return Values
- Scope and Lifetime of Variables
Session 9: Introduction to NumPy and Pandas for Data Manipulation
- Overview of NumPy
- NumPy Arrays and Operations
- Overview of Pandas
- DataFrames and Series
- Data Manipulation with Pandas (selecting, filtering, merging)
- Basic Data Cleaning Techniques
Week 3
Session 7: Programming Fundamentals
- Variables and Data Types
- Control Flow (if statements, loops)
- Basic Input and Output
- Writing and Running Simple Programs
Session 8: Working with Lists, Dictionaries, and Functions
- Lists and List Operations
- Dictionaries and Dictionary Operations
- Defining and Calling Functions
- Function Parameters and Return Values
- Scope and Lifetime of Variables
Session 9: Introduction to NumPy and Pandas for Data Manipulation
- Overview of NumPy
- NumPy Arrays and Operations
- Overview of Pandas
- DataFrames and Series
- Data Manipulation with Pandas (selecting, filtering, merging)
- Basic Data Cleaning Techniques
Week 4
Session 10: Exploratory Data Analysis (EDA) Techniques
- Introduction to EDA
- Data Cleaning and Preprocessing
- Identifying Data Patterns and Trends
- Handling Missing Values
- Outlier Detection and Treatment
- Correlation Analysis
Session 11: Data Visualization Best Practices with Matplotlib and Seaborn
- Introduction to Matplotlib
- Basic Plots with Matplotlib
- Customizing Plots in Matplotlib
- Introduction to Seaborn
- Advanced Plots with Seaborn
- Best Practices for Effective Data Visualization
Session 12: Statistical Summaries and Hypothesis Testing
- Descriptive Statistics
- Measures of Central Tendency and Dispersion
- Probability Distributions
- Introduction to Hypothesis Testing
- Types of Hypothesis Tests
- Interpreting Test Results
Week 4
Session 10: Exploratory Data Analysis (EDA) Techniques
- Introduction to EDA
- Data Cleaning and Preprocessing
- Identifying Data Patterns and Trends
- Handling Missing Values
- Outlier Detection and Treatment
- Correlation Analysis
Session 11: Data Visualization Best Practices with Matplotlib and Seaborn
- Introduction to Matplotlib
- Basic Plots with Matplotlib
- Customizing Plots in Matplotlib
- Introduction to Seaborn
- Advanced Plots with Seaborn
- Best Practices for Effective Data Visualization
Session 12: Statistical Summaries and Hypothesis Testing
- Descriptive Statistics
- Measures of Central Tendency and Dispersion
- Probability Distributions
- Introduction to Hypothesis Testing
- Types of Hypothesis Tests
- Interpreting Test Results
Week 4
Session 10: Exploratory Data Analysis (EDA) Techniques
- Introduction to EDA
- Data Cleaning and Preprocessing
- Identifying Data Patterns and Trends
- Handling Missing Values
- Outlier Detection and Treatment
- Correlation Analysis
Session 11: Data Visualization Best Practices with Matplotlib and Seaborn
- Introduction to Matplotlib
- Basic Plots with Matplotlib
- Customizing Plots in Matplotlib
- Introduction to Seaborn
- Advanced Plots with Seaborn
- Best Practices for Effective Data Visualization
Session 12: Statistical Summaries and Hypothesis Testing
- Descriptive Statistics
- Measures of Central Tendency and Dispersion
- Probability Distributions
- Introduction to Hypothesis Testing
- Types of Hypothesis Tests
- Interpreting Test Results
Week 5
Session 13: Scikit-learn for Machine Learning Algorithms
- Introduction to Scikit-learn
- Supervised Learning Algorithms
- Unsupervised Learning Algorithms
- Model Evaluation and Validation
- Hyperparameter Tuning
Session 14: Pandas Advanced Techniques for Data Wrangling and Cleaning
- Advanced DataFrame Operations
- Merging and Joining DataFrames
- Handling Missing Data
- Data Transformation and Manipulation
- Efficient Data Cleaning Techniques
Session 15: TensorFlow or PyTorch for Basic Deep Learning Concepts
- Introduction to TensorFlow/PyTorch
- Building Neural Networks
- Training and Evaluating Models
- Basic Convolutional Neural Networks (CNNs)
- Implementing Simple Deep Learning Projects
Week 5
Session 13: Scikit-learn for Machine Learning Algorithms
- Introduction to Scikit-learn
- Supervised Learning Algorithms
- Unsupervised Learning Algorithms
- Model Evaluation and Validation
- Hyperparameter Tuning
Session 14: Pandas Advanced Techniques for Data Wrangling and Cleaning
- Advanced DataFrame Operations
- Merging and Joining DataFrames
- Handling Missing Data
- Data Transformation and Manipulation
- Efficient Data Cleaning Techniques
Session 15: TensorFlow or PyTorch for Basic Deep Learning Concepts
- Introduction to TensorFlow/PyTorch
- Building Neural Networks
- Training and Evaluating Models
- Basic Convolutional Neural Networks (CNNs)
- Implementing Simple Deep Learning Projects
Week 5
Session 13: Scikit-learn for Machine Learning Algorithms
- Introduction to Scikit-learn
- Supervised Learning Algorithms
- Unsupervised Learning Algorithms
- Model Evaluation and Validation
- Hyperparameter Tuning
Session 14: Pandas Advanced Techniques for Data Wrangling and Cleaning
- Advanced DataFrame Operations
- Merging and Joining DataFrames
- Handling Missing Data
- Data Transformation and Manipulation
- Efficient Data Cleaning Techniques
Session 15: TensorFlow or PyTorch for Basic Deep Learning Concepts
- Introduction to TensorFlow/PyTorch
- Building Neural Networks
- Training and Evaluating Models
- Basic Convolutional Neural Networks (CNNs)
- Implementing Simple Deep Learning Projects
Week 6
Session 16: Probability Distributions and Statistical Inference
- Introduction to Probability Distributions
- Types of Probability Distributions
- Properties and Applications
- Statistical Inference Concepts
- Confidence Intervals
- Hypothesis Testing Basics
Session 17: Regression Analysis and Hypothesis Testing
- Introduction to Regression Analysis
- Linear Regression
- Multiple Regression
- Logistic Regression
- Advanced Hypothesis Testing
- P-values and Significance Levels
- Interpreting Regression Results
Session 18: Dimensionality Reduction Techniques
- Introduction to Dimensionality Reduction
- Principal Component Analysis (PCA)
- Singular Value Decomposition (SVD)
- Linear Discriminant Analysis (LDA)
- Applications of Dimensionality Reduction
- Choosing the Right Technique
Week 6
Session 16: Probability Distributions and Statistical Inference
- Introduction to Probability Distributions
- Types of Probability Distributions
- Properties and Applications
- Statistical Inference Concepts
- Confidence Intervals
- Hypothesis Testing Basics
Session 17: Regression Analysis and Hypothesis Testing
- Introduction to Regression Analysis
- Linear Regression
- Multiple Regression
- Logistic Regression
- Advanced Hypothesis Testing
- P-values and Significance Levels
- Interpreting Regression Results
Session 18: Dimensionality Reduction Techniques
- Introduction to Dimensionality Reduction
- Principal Component Analysis (PCA)
- Singular Value Decomposition (SVD)
- Linear Discriminant Analysis (LDA)
- Applications of Dimensionality Reduction
- Choosing the Right Technique
Week 6
Session 16: Probability Distributions and Statistical Inference
- Introduction to Probability Distributions
- Types of Probability Distributions
- Properties and Applications
- Statistical Inference Concepts
- Confidence Intervals
- Hypothesis Testing Basics
Session 17: Regression Analysis and Hypothesis Testing
- Introduction to Regression Analysis
- Linear Regression
- Multiple Regression
- Logistic Regression
- Advanced Hypothesis Testing
- P-values and Significance Levels
- Interpreting Regression Results
Session 18: Dimensionality Reduction Techniques
- Introduction to Dimensionality Reduction
- Principal Component Analysis (PCA)
- Singular Value Decomposition (SVD)
- Linear Discriminant Analysis (LDA)
- Applications of Dimensionality Reduction
- Choosing the Right Technique
Download Complete 3 Months Plan
Download Complete 3 Months Plan
CAREER DEVELOPMENT TRACK
Pregrad Career Assist Access
Mentoring
career-specific resume tailoring
Personal Branding
Build and showcase your skills in public
Strategic LinkedIn profiling
Community Session
Strengthen Communication
Improve presentation skills
Interview Preparation
Mock community sessions & GD
Art of negotiation
Domain workshops/Masterclasses
Masterclasses from professionals
HR Session
Career Kick-start
Internship/Freelance/ Applications & Interview
Placement assistance in final year
Total Fee of the Program
₹ 20060/- Including tax
(Non-refundable)
0% cost EMI Option Available*
EMI options for admission will not be available on discounted Fee or admission through scholarship
APPLY NOW
Live Learning delivered by Industry veteran
Sessions Backup
Hands-On Projects & Challenges
Global Certifications
Access to Career Assist cell*
Payment gateway Razorpay
FAQ
Is this course for Beginner, Intermediate, and Advanced level?
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Is this course for Beginner, Intermediate, and Advanced level?
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G Block Sector 6, Noida, Uttar Pradesh - 201301
hello@pregrad.in
Programs
Community
Join Community
Request offline session
Masterclasses
Workshops