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Data Analytics AI and ML
Are you ready to transform yourself with data-driven decisions? If you are a curious beginner or data professional, this AI ML data analytics combo course for you. Artificial intelligence, Machine Learning, and Data analytics are the driving force behind innovation, decision-making, and problem-solving across diverse industries. This combo course is specifically designed to help you with fundamental knowledge and practical skills required to become a proficient data-driven professional. By completing this course you will be able to use technologies into your work, or build a career in this high-demand field. With the holistic learning experience at Win in Life Academy, your journey starts with the essential concepts of data collection, cleaning, and analysis, delve into the fascinating world of machine learning algorithms, and explore how artificial intelligence brings intelligent automation and prediction to life.
Program Details
Key Takeaways
- End-to-End Proficiency: Gain a holistic understanding of the entire data lifecycle, from data acquisition and preparation to building, deploying, and evaluating AI/ML models.
- Practical Skill Development: The course emphasizes hands-on experience with essential tools and techniques in Python, machine learning algorithms, deep learning, and model deployment strategies.
- Problem-Solving Focus: Learn how to leverage AI, ML, and data analytics to address real-world challenges, extract meaningful insights, and drive data-informed decision-making.
- Career Advancement Potential: Equip yourself with the in-demand expertise needed to excel in various roles within the rapidly growing fields of AI, machine learning, and data analytics.
Learning Summaries
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Data Analytics AI Machine Learning
Enrolling in data analytics AI machine learning combo course offers numerous advantages in recent data-driven world. This interdisciplinary skillset is highly sought after across various industries, leading to a wide range of well-paying job roles such as Data Scientists, Machine Learning Engineer, AI Specialist, Business Intelligence Analyst, and more.
- Versatile and Future-Proof Skillset: The AI ML Data Analytics combo course provides a strong foundation in data handling, analytical techniques, and intelligent system development, making graduates adaptable to technologies and various industry needs.
- Comprehensive Understanding and Synergy: This combo course allows you for a deeper understanding of how they interrelate. The combination provides the foundation for data-driven insights, predictive modelling and automation, and boarder goal of creating intelligent systems.
- Problem-solving and Innovation Capabilities: The course equips you with the skills to analyze complex datasets, identify patterns, build intelligent solutions, and communicate data-driven insights effectively. This fosters critical thinking and collaborative opportunities to innovation.
I'm Interested in this program?
Certifications for Data Analytics AI and ML Combo Course
Once you completed data analytics AI and ML combo course, you will get globally recognized certification from Win in Life Academy.
Globally Recognised Certification
Educational Board of Vocational Training and Research
Note: To secure your certification for Data Analytics AI ML combo course certificate, you must successfully complete the training from Win in Life Academy.
Tools to be Covered in Data Analytics AI ML combo Course
mlFlow
Python
Docker
ChatGPT
ChatGPT
TensorFlow
AWS
tableau
Microsoft Excel
Rest-API
Power BI
R Language
NumPY
Dall-E
- Disclaimer: The tools, software, platforms and datasets presented within the AI Machine Learning Data Science Combo Course are provided solely for educational purposes to facilitate learning and understanding of relevant concepts and techniques. It should not be considered suitable or optimized for real-world production environments.
- These tools may have limited functionality, trial version, or specific configurations for educational use and may involve inherent risks such as data loss or system instability, for which we offer no guarantees or warranties regarding their performance, reliability, accuracy or suitability beyond their educational demonstration.
Master 20+ essential industry tools
Data Analytics AI and ML Combo Course Curriculum
Expert Designed Data Analytics AI and ML Combo Course for Students and Professionals
A comprehensive Data Analytics AI Machine Learning combo course certification, crafted by industry experts will propel you towards your ideal career.
Pre AI and ML Course Curriculum
Modules
Non-Technical
Module 1
English Communication & Grammar
- Basics of Communication
- Grammar and Vocabulary
- Verbal Communication Skills
- Written Communication
- Non-verbal Communication
- Confidence and Public Speaking
- Professional Communication Etiquette
- Practical Application
- Continuous Learning
Module 2
Mock Interviews
(Practice Assessment Test)
- Personal Introduction
- Technical Skills
- Problem-Solving
- Behavioral Insights
- Industry Knowledge
- Hypothetical Scenarios
- Soft Skills
Module 3
Corporate Etiquette
- Making the Most of Meetings
- Dressing Right for the Workplace
- Being Respectful and Thoughtful at Work
- Keeping Your Workspace Clean and Organized
- Using Technology Responsibly
- Dining with Manners in Professional Settings
- Building Relationships Through Networking
- Best Practices for Virtual Meetings and Online
- Communication
- Managing Your Time and Meeting Deadlines
- Understanding and Respecting Cultural Differences
- Leaving a Job Gracefully
Module 4
Aptitude
Program Modules for Data Analytics Course
This comprehensive curriculum is designed to take you from the fundamentals of data handling to advanced data analysis and visualization techniques. It's structured across seven modules to provide a progressive and thorough learning experience:
Module 1
- 3 weeks
Excel Basic to Advanced
This foundational module will equip you with essential and advanced Excel skills. You'll learn how to efficiently manage, manipulate, and analyse data using Excel's powerful features, laying the groundwork for more complex data tasks.
- Introduction to the Excel interface (Ribbon, Quick Access Toolbar, Backstage View)
- Navigating worksheets and workbooks
- Data entry and editing
- Basic formatting (fonts, alignment, number formats)
- Working with cells, rows, and columns
- Basic formulas and functions (SUM, AVERAGE, COUNT, MIN, MAX)
- Saving and opening workbooks
- Printing worksheets
Intermediate Excel:
- Working with more complex formulas and functions (IF, VLOOKUP, HLOOKUP, INDEX, MATCH)
- Data validation
- Conditional formatting
- Creating and formatting charts and graphs
- Working with tables
- Sorting and filtering data
- PivotTables and PivotCharts for data summarization and analysis
- Working with multiple worksheets and workbooks
- Advanced formulas and array formulas
- Working with text functions (LEFT, RIGHT, MID, CONCATENATE)
- Date and time functions
- Logical functions (AND, OR, NOT)
- Data analysis tools (Goal Seek, Scenario Manager, Solver)
- Statistical functions (STDEV, VAR, CORREL)
- Introduction to Macros and VBA (Visual Basic for Applications)
- Importing and exporting data from various sources
- Power Query (Get & Transform Data) for data cleaning and shaping
Highlights
Module 2
- 3 weeks
Data Toolkit
This module introduces you to a collection of essential tools and techniques crucial for working with data. It will likely cover concepts and software that complement Excel and prepare you for more specialized data handling.
- What is data? Types of data (structured, unstructured, semi-structured)
- Data sources and collection methods
- Data quality and its importance
- Data ethics and privacy considerations
- Spreadsheets (Excel, Google Sheets) – potentially a brief review or focus on advanced features not covered in Module 1.
- Database Management Systems (DBMS) – Introduction to concepts (tables, records, fields) and possibly a brief overview of SQL.
- Data Analysis Software (e.g., Python with Pandas, R) – introductory concepts and potential for basic exercises.
- Data Visualization Tools (Tableau, Power BI) – high-level overview, preparing for later modules.
- Cloud-based data platforms (e.g., Google Cloud, AWS, Azure) – basic awareness.
- Data storage and organization principles
- Data governance and security basics
- Data lifecycle management
Highlights
Module 3
- 3 weeks
Data Analytics
Building upon the previous modules, this section delves into the core principles of data analytics. You'll learn how to extract meaningful insights from data, identify trends, and make data-driven decisions using various analytical methods.
- The data analysis process (define, collect, clean, analyze, interpret, communicate)
- Types of data analysis (descriptive, diagnostic, predictive, prescriptive)
- Formulating analytical questions
- Identifying key performance indicators (KPIs)
- Techniques for summarizing data (descriptive statistics, frequency distributions)
- Visualizing data to identify patterns and anomalies (histograms, scatter plots, box plots)
- Identifying missing values and outliers
- Understanding data distributions
- Correlation analysis
- Trend analysis
- Comparative analysis
- Segmentation analysis
- Basic forecasting techniques
- Drawing conclusions from data analysis
- Presenting findings effectively using visuals and narratives
- Understanding the limitations of data analysis
Highlights
Module 4
- 3 weeks
Statistics
This module provides the statistical knowledge necessary for robust data analysis. You'll understand key statistical concepts, learn how to apply them to real-world data, and gain the ability to interpret statistical results effectively.
- Measures of central tendency (mean, median, mode)
- Measures of dispersion (range, variance, standard deviation, interquartile range)
- Understanding distributions (normal, skewed, etc.)
- Visualizing distributions (histograms, box plots)
- Populations and samples
- Sampling methods
- Central Limit Theorem
- Confidence intervals
- Hypothesis testing (null and alternative hypotheses, p-values, significance level)
- Common statistical tests (t-tests, ANOVA, chi-square tests)
- Understanding correlation (positive, negative, no correlation)
- Simple linear regression (fitting a line to data, interpreting coefficients)
- Introduction to multiple regression (concepts)
- Basic probability concepts
- Conditional probability
- Bayes’ Theorem (potentially)
Highlights
Module 5
- 3 weeks
Data Wrangling with SQL
This module focuses on the critical skill of data wrangling using SQL (Structured Query Language). You'll learn how to extract, clean, transform, and prepare data stored in databases, making it ready for analysis and visualization.
- Relational database concepts (tables, schemas, keys)
- SQL basics (data types, operators)
- Database management systems (overview)
- Basic SELECT queries
- Filtering data with WHERE clause
- Sorting data with ORDER BY clause
- Selecting distinct values
- Using aggregate functions (COUNT, SUM, AVG, MIN, MAX)
- Grouping data with GROUP BY clause
- Filtering grouped data with HAVING clause
- Understanding different types of joins (INNER, LEFT, RIGHT, FULL)
- Writing JOIN clauses
- Inserting, updating, and deleting data (INSERT, UPDATE, DELETE statements)
- Creating and altering tables (CREATE TABLE, ALTER TABLE, DROP TABLE)
- Using string functions (e.g., SUBSTRING, UPPER, LOWER, TRIM)
- Working with date and time functions
- Handling NULL values
- Using CASE statements for conditional logic
- Writing subqueries
- Understanding and using CTEs for complex queries
Window Functions (Introduction):
Overview and basic applications for ranking and aggregation within partitions.
Highlights
Module 6
- 3 weeks
Tableau
You'll be introduced to Tableau, a powerful data visualization tool. This module will teach you how to create interactive dashboards and compelling visual representations of your data, enabling effective communication of insights.
- Tableau interface and terminology
- Connecting to various data sources
- Understanding dimensions and measures
- Creating different chart types (bar charts, line charts, scatter plots, pie charts, etc.)
- Working with marks card (color, size, shape, label, detail, tooltip)
- Using shelves (rows, columns, filters, pages)
- Sorting and filtering data
- Grouping and binning data
- Creating calculated fields
- Using parameters
- Working with hierarchies
- Creating dual-axis charts
- Using maps for geospatial analysis
- Building dashboards and stories
- Interactive elements (actions, filters)
Formatting and Annotations:
- Customizing visualizations for clarity and aesthetics
- Adding annotations and mark labels
Sharing and Exporting:
- Saving and sharing workbooks
- Publishing to Tableau Server or Tableau Public
- Exporting visualizations
Highlights
Module 7
- 3 weeks
Visualization using Power BI
The final module focuses on Microsoft's Power BI, another leading data visualization platform. You'll learn to connect to various data sources, build interactive reports, and create insightful visualizations to share your data stories effectively.
- Power BI Desktop interface
- Connecting to various data sources
- Understanding fields, measures, and calculated columns
- Creating different chart types (bar charts, line charts, scatter plots, pie charts, maps, etc.)
- Working with the Visualizations pane (fields, formatting)
- Using filters and slicers
- Understanding relationships between tables
- Creating and managing relationships
- Introduction to DAX (Data Analysis Expressions) for calculations
- Creating combo charts and dual-axis charts
- Using advanced chart types (treemaps, funnel charts, gauge charts)
- Building interactive dashboards and reports
- Using drill-down and drill-through features
- Publishing reports and dashboards to the Power BI Service
- Sharing and collaborating on dashboards and reports
- Creating and managing workspaces
- Understanding data refresh options
- Understanding DAX syntax and functions
- Creating basic measures and calculated columns
Highlights
Module 8
- 3 weeks
Code Optimization
Build integrated data dashboards using Python, Excel, and MySQL. Learn code optimization techniques and implement robust error handling.
- Integrating Python, Excel, and MySQL to create a data dashboard
- Using visualization libraries (Matplotlib, Seaborn)
- Connecting the application to MySQL for data storage
- Techniques for optimizing Python and SQL code
- Implementing robust error handling in Python and SQL
- Work on a comprehensive project integrating Python, Excel, and MySQL
- Evaluation Of a Model
- Practical
- Data Dashboard Development
- Code Optimization
- Error Handling
- Data Visualization
- Database Integration
Highlights
Our Distinctive Approach
We deliver an exceptional AI ML and Data Analytics journey by blending advanced tools, meticulously crafted curriculum, and guidance from seasoned industry experts.
Applied Learning & Real-World Projects
We prioritize practical application through extensive hands-on projects, solidifying theoretical understanding and mirroring current industry practices.
Expert Mentorship from Leading Professionals
Our instructors provide deep insights and stimulate critical thinking, ensuring your knowledge is aligned with the latest AI/ML advancements, establishing a new standard in education.
Career Advancement & Networking
As a graduate of our program, you gain access to valuable networking opportunities and career resources, empowering you to secure impactful roles and internships within the AI/ML field.
Program Fees
New Batches Starts Every 15th & 30th
₹70,000 (*Incl. Taxes)
Note: 0% interest rates with no hidden cost
Programme Faculty
What's Unique About This Program?
Why is our AI ML Diploma the top choice?
Features
Industry-Focused Curriculum
Placement mentorship program
Corporate Etiquette Sessions
Capstone projects
LMS Course kit
EC Council collaboration
Recorded Video
1:1 Personalized Mentorship
Placement Mock Interviews
Interdisciplinary expertise
Industry Expert sessions
WILA
Institute 1
Institute 2
Institute 3
Success Stories
Graduate Perspectives
Win in Life’s Data Analytics AI and ML Accreditations
Best Data Analytics AI and ML Combo Course training by experienced faculty and industry leaders in the form of pre-recorded videos, projects, assignments, and live interactive sessions.
Connect with our graduates
Have questions? Reach out to our alumni!
Find WILA alumni profiles and know more about their career path, specialisation and more.
Frequently Asked Questions (FAQs)
An AI ML and Data Analytics Combo Course is a comprehensive program designed to equip individuals with skills in Artificial Intelligence (AI), Machine Learning (ML), and Data Analytics. It integrates these three interconnected fields into a single learning experience.
This AI ML and Data Analytics Combo Course is ideal for individuals looking to build a career in data science, AI engineering, business intelligence, or related fields. It suits both beginners with a foundational understanding and professionals seeking to upskill in Data Analytics AI and ML.
A basic understanding of mathematics (including statistics and linear algebra) and some programming knowledge is beneficial for this AI ML and Data Analytics Combo Course. Specific requirements will be outlines by the course provider.
In this Data Analytics AI ML course, you will learn the fundamentals and advanced techniques of Data Analytics, along with the principles and practical applications of AI and ML.
The structure of an AI ML and Data Analytics Combo Course typically involves modules covering Data Analytics fundamentals, statistical analysis, machine learning algorithms, deep learning concepts, and AI applications.
Graduates of an AI ML and Data Analytics Combo Course can pursue roles such as Data Scientist, Machine Learning Engineer, Data Analyst, Business Intelligence Analyst, AI Researcher, and more.
The duration of an AI ML and Data Analytics Combo Course can vary depending on the format (full-time, part-time, online) and the depth of the curriculum. It can range from a few months to over a year.
Yes, most effective AI ML and Data Analytics Combo Courses include significant hands-on projects and real-world case studies to provide practical experience.
A typical Data Analytics AI ML program will cover tools and libraries such as Python, R, SQL, Pandas, NumPy, Scikit-learn, TensorFlow, Keras, and various data visualization tools.
A well-designed AI ML and Data Analytics Combo Course will have a balance of theoretical concepts and practical application through coding exercises and projects.
This course emphasizes how Data Analytics provides the foundation for AI and ML. You’ll learn how to collect, clean, analyze, and prepare data that is crucial for training and deploying AI ML models.
Yes, the course will likely cover various types of Data Analytics, including descriptive, diagnostic, predictive, and prescriptive analytics, and how they relate to AI and ML.
A comprehensive Data Analytics AI ML program will cover a range of machine learning algorithms, including supervised learning (regression, classification), unsupervised learning (clustering, dimensionality reduction), and potentially reinforcement learning.
Depending on the level and specialization, an AI ML and Data Analytics Combo Course may include modules on Deep Learning concepts and frameworks like neural networks.
This Data Analytics AI Machine Learning course will use case studies, industry-relevant projects, and practical exercises to simulate real-world data challenges and problem-solving scenarios.
Yes, data visualization is a crucial component of Data Analytics, and this AI ML and Data Analytics Combo Course will equip you with the skills to effectively communicate insights through visual representations.
