CloudyML's Data Analytics
Work Experience Live Program
6 months live training + 6 months real industry work with dedicated career support to get you job-ready.
get details on whatsapp!6 months live training + 6 months real industry work with dedicated career support to get you job-ready.
get details on whatsapp!
In every 3 hours live session, along with learning, you will be solving problems live to get better and clear understanding of the concept.
Get 1-1 Chat Support for Doubt Clearance daily between 6PM - 10PM. Also get Live Doubt Support over Zoom Meeting between 8PM-9PM
Along with the Live sessions, get Assignments & Quizzes to practice your skill and boost your confidence.
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Data Analyst @Amazon
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Sr. Data Analyst @Oracle
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Data Analyst @Uber
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Business Analyst @Razorpay
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Jr. Data Analyst @Freshworks
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Jr. Data Engineer @InMobi
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Sr. Data Analyst @HSBC
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Data Analyst @EY
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Business Analyst @Web Spiders
More than 30000+ learners are getting benefits from our course and we are helping them to achieve their dreams by enhancing their skills to Supreme Level with this Roadmap.
Week 1: Introduction to Python
- Overview of Python, use cases, Google Colab setup.
- Print statements, comments, variables, data types, typecasting.
- String operations and math functions.
Week 2: Data Structures & Control Flow
- Introduction to Data Structures.
- Lists: operations, methods, and manipulation.
- Conditional statements: if, elif, else.
- Loops: for and while loops.
Week 3: Advanced Structures & Functions
- Functions: defining, calling, and arguments.
- Tuples, Sets, and Dictionaries.
Week 4: Advanced Python Concepts
- List, Dictionary, and Set comprehensions.
- Recursion and introduction to Object-Oriented Programming (OOPS).
- Basics of Regex (Regular Expressions).
Module 1 Case Studies
Week 5: Introduction to NumPy
- Introduction to NumPy, List vs. NumPy arrays.
- NumPy array indexing, reshaping, and slicing.
- NumPy view vs. copy, hstack vs. vstack, concatenation, insert, append, delete.
Week 6: Introduction to Pandas
- Introduction to Pandas: Series and DataFrame.
- Pandas concatenation, apply method.
- Indexing and selecting data: loc and iloc.
Week 7: Data Cleaning & Visualization
- Introduction to data cleaning: handling NaN cases and missing values.
- Imputation techniques.
- Introduction to Exploratory Data Analysis (EDA).
- Visualization with Matplotlib and Seaborn.
Week 8: Python Data Analysis Project
- End-to-end industry-level project using NumPy, Pandas, and visualization libraries to clean, analyze, and present findings from a complex dataset.
Module 2 Case Studies (EDA & Cleaning)
Week 9:Foundations
- Algebra: Linear equations, systems of equations, exponents, and logarithms.
- Discrete Mathematics: Combinatorics and probability.
Week 10: Descriptive & Inferential Statistics
- Descriptive Statistics: Measures of central tendency (mean, median, mode) and dispersion (variance, standard deviation).
- Data visualization (histograms, boxplots).
- Inferential Statistics: Probability distributions (normal, binomial), sampling.
Week 11: Statistical Analysis
- Hypothesis testing (t-tests, chi-square tests).
- Correlation (Pearson, Spearman) and Regression Analysis (Simple and Multiple Linear Regression).
- Confidence intervals and Analysis of Variance (ANOVA).
Module 3 Case Studies (Statistical Testing)
Week 12: SQL Fundamentals
- Introduction to databases, ACID properties, MySQL Workbench.
- Data types, DDL, DML, and constraints.
- Clauses: WHERE, GROUP BY, HAVING, ORDER BY, TOP/LIMIT.
Week 13: Advanced SQL Joins & Subqueries
- Subqueries (Scalar, Multi-row, Correlated).
- Joins: INNER, LEFT, RIGHT, FULL OUTER, SELF, CROSS.
Week 14: Advanced SQL Functions
- String and Date-Time manipulation.
- CASE WHEN statements.
- Common Table Expressions (CTE & Recursive CTE).
Week 15: SQL for Analytics Project
-Industry-level project involving complex, multi-table queries, analysis, and data extraction to solve a business problem.
Week 16: Practical implementation
- Building logistic regression models using Python
Module 4 Case Studies (Multi-table Data Analysis)
Week 17: Excel Basics & Functions
- Excel interface, data types, relative/absolute references.
- Date-Time functions, formatting, charts (column, pie, line).
- Logical functions: IF, IFS, AND, OR, NOT.
- Basic functions (COUNTA, LARGE, etc.) and conditional formatting.
Week 18: Advanced Excel & Dashboards
- Developer options, CHOOSE function, Named Ranges, OFFSET.
- Pivot Tables, Pivot Graphs, Slicers, and Timelines.
- Lookup Functions (VLOOKUP, HLOOKUP, XLOOKUP), Database functions.
- Introduction to Power Query and Power Pivot for dashboard preparation.
Module 5 Case Studies (Excel Dashboard)
Week 19: Introduction to Power BI & Visuals
- Introduction to BI and Power BI.
- Basic & Advanced Charts (Bar, Pie, Line, Scatter, Treemap, Maps).
- Tables, Conditional Formatting, Matrix, Cards, Filters, Slicers.
Week 20: Power Query & Data Modeling
- Creating and publishing reports, Power BI Dashboards.
- Power Query: Adding/Removing rows, text/number/date transformations.
- Appending and merging sheets, conditional columns, GroupBy.
- Data Modeling: Relationships, Normalization, OLTP vs. OLAP.
Week 21: DAX & Advanced Power BI
- Introduction to DAX: Date, Text, and Logical functions.
- Connecting to SQL, Web Data, and OData.
- Introduction to M Language.
- Row-Level Security (RLS): Static and Dynamic.
Week 22: End-to-End Power BI Project
- Industry-level project to build a comprehensive, multi-page interactive dashboard from source data (e.g., SQL DB) to final published report.
Module 6 Case Studies (End-to-End Power BI Report)
Week 23: Applied Generative AI for Analytics
- Foundations: Generative AI vs. Predictive ML, overview of LLMs.
- Prompt Engineering Basics: Zero-shot, Few-shot, Chain-of-thought.
- Applying GenAI: Building a text summarizer or sentiment analyzer.
- Structured output (JSON) for analytics and connecting LLMs to data (Q&A over CSV/SQL).
- Introduction to RAG (Retrieval-Augmented Generation) concepts.
Module 7 Case Studies (Building a GenAI-powered tool)
Week 24: Linear Regression
- Overview of Machine Learning types (Supervised, Unsupervised).
- Simple and Multiple Linear Regression.
- Model evaluation metrics: MSE, RMSE, R-squared.
- Feature selection and engineering basics
Week 25:Logistic Regression
- Introduction to classification problems.
- Logistic Regression: Binary and Multi-class.
- Model interpretation and evaluation.
- Confusion matrix and metrics (Accuracy, Precision, Recall, F1-score).
Week 26: Predictive Modeling Project
- Industry-level project to build, train, and evaluate a predictive model (regression or classification) and present the findings.
Module 8 Case Studies (Predictive Modeling)
1. Census Salary Data Analysis
2. Supply Chain Analytics
3. IPL Data Analysis
4. COVID 19 Analysis
5. Loan Application Analysis
6. Superstore Analysis
Introduction to Tableau , Tableau Installation , User Interface , Dimensions and Measures, How to Prepare Charts using Tableau, Line Charts, Combined Axis and Area Charts, Dual Axis Charts
Working with Data , Properties of Fields , Dimension Filters , Measure Filters , Visual Filters, Sets , Parameters , Groups , Calculated Fields
Date Functions, Text Functions , Bins and Histogram , Sort Function
Introduction to Dashboard , Objects in Dashboard , Filters in Dashboard , Actions , Dashboard for Mobile , Story , Dashboard Interactivity
Union, Joins , Data Blending, Fixed LOD , Include LOD , Exclude LOD , Advanced Techniques
Some companies keep first round as aptitude to check thinking skills. In this course we have covered aptitude topics with their examples which are widely asked in aptitude round of interviews. This course is divided into Quantitative, logical and verbal aptitude sections. It will help learners to build logic on all 3 levels of aptitude.
(Hurry Up! Limited Time Offer)
+ 18% GST
Data Scientist || with 7+ years of industry experience
Engineering Manager || with 10+ years of industry experience
Data Analyst || with 6+ years of industry experience
Data Science Trainer || with 5+ years of industry experience
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