- Program Highlights
The Professional Certification in Data Science and Engineering Analytics is a comprehensive program designed for engineers and analysts focusing on data-driven decision-making in the automotive and electrical engineering industries. Covering Python programming, statistical methods, visualization, and dashboard development, this program equips participants to analyze, visualize, and interpret data for predictive maintenance, energy optimization, and engineering insights.
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- Admission Closes on 1st Nov
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- Career Opportunities
- Data Analysis for Automotive and Electrical Systems: Extract, clean, and analyze data from automotive and electrical sources to derive actionable insights.
- EV Battery Health Monitoring and Optimization: Analyze battery performance to improve life cycles and predict failures using data analytics.
- Predictive Maintenance Modeling for Automotive Systems: Develop models to foresee and prevent system failures, ensuring operational efficiency.
- Engineering Data Visualization and Dashboard Design: Create intuitive visual representations of data to support engineering decision-making.
- Energy Consumption Analytics for EVs: Monitor and optimize energy use in EV systems through data-driven approaches.
- Sales and Market Data Segmentation in EV Applications: Analyze market trends and segment sales data for strategic planning.
- Smart Grid Interaction Analytics for EVs: Evaluate EV integration with smart grids to optimize energy distribution and grid stability.
- Statistical Modeling for Engineering Datasets: Apply advanced statistical techniques to analyze engineering datasets and predict trends.
- Algorithm Development for Engineering Tools: Design algorithms for tools that enhance automotive and electrical system efficiency.
- Fleet and Energy Management Using Dashboards: Create dashboards to manage EV fleets and optimize energy distribution in real time.
- Data Analyst for Automotive Systems: Analyze large datasets to improve vehicle performance and operational efficiency.
- Predictive Maintenance Specialist: Develop systems that forecast equipment failures and optimize maintenance schedules.
- Battery Performance Analyst: Focus on data trends to extend battery life and enhance energy efficiency.
- Python Developer for Engineering Applications: Build Python-based solutions for electrical and automotive engineering challenges.
- Energy Dashboard Designer: Create dashboards that provide real-time insights into energy usage and performance metrics.
- Statistical Data Scientist: Apply statistical methods to solve complex problems in automotive and electrical engineering.
- Market Segmentation Analyst: Study market data to identify patterns and inform business strategies.
- Smart Grid Interaction Specialist: Analyze and optimize EV interactions with smart grid systems for sustainable energy use.
- Engineering Insights Specialist: Generate actionable insights from complex engineering datasets to support decision-making.
- Algorithm Developer for Electrical and Mechanical Systems: Create algorithms for predictive analytics and system optimizations in engineering applications.
- Python Programming for Data Analytics and Engineering: Proficiency in writing, testing, and deploying Python-based solutions tailored for engineering applications.
- Proficiency in NumPy and pandas for Data Manipulation: Expertise in handling and transforming large datasets using Python libraries.
- Visualization Skills Using Matplotlib and Power BI: Ability to create detailed charts and dashboards for data representation.
- Statistical Analysis Techniques for Predictive Modeling: Knowledge of advanced statistical methods to build reliable predictive models.
- Dashboard Design and Development for EV Applications: Competence in creating interactive dashboards for real-time data monitoring.
- Data Cleaning and Preprocessing for Engineering Datasets: Skills in refining raw data for analysis and model building.
- Time-Series Analysis for Battery Health and Performance: Ability to study and predict battery trends using time-series techniques.
- Feature Engineering for Energy Optimization: Crafting new features from data to improve energy efficiency models.
- Regression and Correlation Analysis for Predictive Modeling: Applying statistical techniques to predict system behaviors and outcomes.
- Integration of Analytics and Dashboards for Decision-Making: Combining data insights with visual tools to inform strategic decisions.
- Tata Motors
- Mahindra Electric
- Ola Electric
- Hero Electric
- Ather Energy
- Bosch India
- Ashok Leyland
- TVS Motor Company
- Continental Automotive
- Hyundai Motor India
- L&T Technology Services
- KPIT Technologies
- Wipro Automotive
- Siemens India
- Exicom Power Solutions
- FOR ENTERPRISE
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- Program Outcomes
- Program Curriculum
Module 1: Python Programming for Engineers: Applications in Electrical and Mechanical Systems
- Module Description:
- This module introduces Python programming for data analysis, covering basics to intermediate topics:
- Module Details:
- Understanding Python and Its Popularity
- Introduction to Data Analysis
- Why Python is Ideal for Data Analysis
- Role of Data Analysts and Their Tasks
- Application Areas of Data Analytics
- Pre-requisites and Software
- Requirements for Python in Data Analysis
- Introduction to Python Programming: The Basics
- Building a Strong Foundation in Python
- Customizing Python Code and Writing Functions
- Testing and Validating Python Code
- Control Flow in Python: Loops and Conditional Statements
- Strings: The Basics of Python Intermediate
- Files: Diving into Real-World Scenarios
- Lists: The Smarter Strings
- Dictionaries and Tuples in Python
- Project Work: Building Electric Vehicle Parameter Calculator System with Python
- Data Modeling and Algorithm Development: Performing EV formula mapping
- Implementation and Testing: Coding the Calculator in Python
- Evaluation and Refinement: Testing Accuracy and Usability, Making Improvements
- Understanding Python and Its Popularity
Module 2: Data Analysis Techniques for Electrical and Mechanical Engineers in the Automotive Industry
- Module Description:
- Participants learn to use NumPy and pandas for data analysis:
- Module Details:
- Numpy Fundamentals: Overview, Installation, Array Basics, Data Types
- Array Operations: Basic and Advanced Operations, Arithmetic and Matrix Products, Universal Functions
- Data Handling: Indexing, Slicing, Iterating, Conditions and Boolean Arrays, Shape and Array Manipulation
- Advanced Numpy Techniques: Joining/Splitting Arrays, Broadcasting, Structured Arrays
- Data Input/Output: Loading/Saving Data, Reading Tabular Data Files
- Statistical Functions: Aggregate Functions, Statistical Analysis
- Memory Management: Copies, Views, Memory Layout
- Examples and Practical Applications: Implementing Real-world Scenarios
- Pandas Basics: Installation, Introduction to Data Structures (Series, DataFrame, Index Objects)
- Handling Data: Reindexing, Dropping, Data Alignment, Arithmetic Operations
- Data Analysis Techniques: Function Application, Statistics, Sorting, Correlation
- Advanced DataFrame Operations: Merging, Concatenating, Combining, Pivoting
- Complex Data Operations: Hierarchical Indexing, Reordering, GroupBy Operations
- Data Cleaning: Removing Duplicates, Filling NaN Values, Filtering Outliers
- Integration and Application: String Manipulations, Regular Expressions, Using Pandas in Projects
- Final Project and Case Studies: Implementing learned skills in comprehensive project scenarios
- Battery Health Analysis Through Time-Series Data
- Charging Infrastructure Optimization: A Spatial Data Approach
- Predictive Maintenance Models for engineering systems
- Data Wrangling for Sensor Anomalies Detection
- Efficiency Enhancement through Feature Engineering in Power Consumption
- Numpy Fundamentals: Overview, Installation, Array Basics, Data Types
Module 3: Data Visualization and Dashboard Design for Electric Vehicle Applications
- Module Description:
- This module focuses on creating visualizations and dashboards:
- Module Details:
- Introduction to the Matplotlib Library
- Chart Essentials: Adding a Grid, Legend
- Creating Various Chart Types: Line Charts, Histograms, Bar Charts, Pie Charts
- Chart Customizations: xticks and yticks
- Saving Charts: Directly as Images, Advanced Saving Options
- Project on Matplotlib: A comprehensive project applying learned skills on real datasets
- Getting Started with Power BI: Introduction
- Navigating Power BI Interface: Overview of the dashboard
- Download and Setup: Installing Power BI Desktop
- Using Power Query Editor: Importing and transforming data
- Data Modeling in Power BI: Creating relationships
- Engineering Data Analysis Part 1: Engineering Sales Data Analysis
- Engineering Data Analysis Part 2: Deeper insights into engineering market segmentation and customer behavior
- Analyzing Battery Performance: Data Analysis with Battery Charging and Discharging Data
- Engineering Sales Data Analysis Part 1: Visualizing sales volume
- Engineering Sales Data Analysis Part 2: Advanced analysis on battery lifespan and warranty claims
- Creating Dashboards: Techniques for effective dashboard design
- Energy Consumption Dashboard: Building a comprehensive dashboard for monitoring energy usage
- Introduction to the Matplotlib Library
Module 4: Statistical Methods for Automotive and Electrical Engineering Data Analysis
- Module Description:
- This module explores statistical concepts for engineering data.
- Module Details:
- Introduction to Probability: Counting methods (permutations and combinations), probability axioms
- Events and Probabilities: Sample space, events, independent and mutually exclusive events, marginal, conditional, and joint probabilities
- Random Variables and Distributions: Random variables, discrete and continuous variables, probability mass and distribution functions
- Common Distributions: Uniform, Bernoulli, binomial, exponential, Poisson, normal, and standard normal distributions
- Special Distributions: T-distribution, chi-squared distributions
- Cumulative Distribution Function: Understanding and application of CDF
- Conditional Probability and Distributions: Conditional PDF, Bayes Theorem, conditional expectation and variance”
- Descriptive Statistics: Mean, median, mode, variance, standard deviation, correlation, and covariance
- Central Limit Theorem: Introduction and implications
- Confidence Intervals: Understanding and confidence for different parameters
- Hypothesis Testing: Z-test, t-test, and chi-squared test, their assumptions
- Statistical Inference Applications: Practical scenarios in automotive and electrical data analysis
- Real-world Applications: Applying statistical methods to industry problems
- Data Analysis Techniques: Advanced correlation and regression techniques for predictive modeling
- Simulation and Computation: Use of statistical software for simulation and computation of real-world data
- Project and Case Studies: Hands-on projects involving statistical analysis on automotive and electrical datasets
- Introduction to Probability: Counting methods (permutations and combinations), probability axioms
- Skills Covered
Python Programming for Data Analytics and Engineering
Proficiency in NumPy and pandas for Data Manipulation
Visualization Skills Using Matplotlib and Power BI:
Statistical Analysis Techniques for Predictive Modeling
Dashboard Design and Development for EV Applications
Data Cleaning and Preprocessing for Engineering Datasets
Time-Series Analysis for Battery Health and Performance
Feature Engineering for Energy Optimization
Regression and Correlation Analysis for Predictive Modeling
Integration of Analytics and Dashboards for Decision-Making
- Projects
Electric vehicle Parameter Calculator using Python
Battery Health Analysis through Time-Series Data
Energy Consumption Dashboard Design
Create a comprehensive dashboard using Power BI and Python’s Matplotlib to visualize and monitor energy consumption patterns in electric vehicles.
Statistical Modeling for Predictive Maintenance in Automotive Systems
Apply statistical methods and regression techniques to develop predictive maintenance models, enhancing efficiency and reliability in automotive systems.
- Benefits
- Build foundational and advanced skills in data analytics.
- Learn practical applications in EV and engineering industries.
- Develop industry-relevant projects for a strong portfolio.
- Gain employability in data-driven engineering roles.
- Understand key concepts in visualization, statistics, and predictive analytics.
- Gain in-depth expertise in data science and analytics for engineering.
- Enhance career opportunities in EV and automotive data roles.
- Hands-on experience with Python, Power BI, and statistical methods.
- Learn industry-relevant techniques for battery health and energy monitoring.
- Build a portfolio with real-world projects tailored for engineering applications.
- Develop Python applications for engineering analytics.
- Perform advanced data analysis using NumPy and pandas.
- Create interactive dashboards using Power BI and Matplotlib.
- Apply statistical methods to automotive and electrical data.
- Analyze battery health and energy consumption trends.
- Develop predictive maintenance models for EV systems.
- Visualize and interpret complex engineering datasets.
- Mode of Learning
Complete on-site
classroom program
Location: Mumbai
LIVE + Recorded + Onsite + Hardware + Workshop
LIVE + Weekend on-site sessions
Location: Pune, Delhi
LIVE + Recorded + Hardware + Workshop
Location: Global
- Tools Covered
Hardware Labs Access
Two-Wheeler Simulator & Test Bench
The 2 Wheeler Simulator & Testbench focuses on evaluating EV battery performance, including voltage, current, discharge profiles, and capacity testing under various load conditions. It also covers Battery Management System (BMS) testing and dynamic load analysis to optimize electric bike performance and safety.
LAB 1
Charging Station Simulator and Test Bench
The Charging Station Simulator and Test Bench covers experiments focusing on the efficiency, behavior, and safety features of EV charging systems. It includes the measurement of voltage, current, and power consumption during charging, and tests security features like RFID and OTP-based authentication. Key areas include energy efficiency analysis, protection unit testing, and charging behavior under different conditions
LAB 2
EV In-house manufacturing & Development KIT
The EV In-house Manufacturing & Development KIT provides all the essential tools and components for building and prototyping electric vehicles, supporting the entire development process from concept to production. It includes equipment for battery monitoring, motor control, system integration, and real-time data analysis, ensuring seamless development for EV projects
LAB 5
