• 3 Hours Interactive Live Weekend Sessions
• Classes on Every Saturday & Sunday
• Get Hands-on Practical Learning Experience
• Mentored Live by Industry Experts
• Work on Real-World Projects & Case Studies
• Learn Pro Job Hunting Techniques
• Get Internship
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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.
Week 1: Overview of python course, Intro to python, usecases, google colab, print statement, comment, variables, datatypes, typecasting, indexing, slicing, string operations, math functions
Week 2: Data Structures introduction, lists, conditional statements
Week 3: loops, functions, tuples, set , dictionary
Week 4: list comprehension, dict & set comprehension, recursion, file handling, error handling, introduction to OOPS
Week 5: Basics of Regex, intro to numpy, list vs numpy, numpy array indexing, numpy array reshaping, numpy slicing operations, numpy view vs copy, numpy hstack vs vstack, numpy concatenation, numpy insert append delete
Week 6: intro to pandas, pandas series, pandas dataframe, pandas concatenation, apply method, indexing dataframe(loc, iloc), pandas groupby, pandas date range
Week 7: Intro to data cleaning, Nan cases, missing values treatment, Imputation techniques, intro to EDA, matplotlib, seaborn, example using case study
Week 8:
- Algebra
- Introduction to algebraic equations and expressions
- Linear equations and systems of equations
- Exponents and logarithms
- Discrete Mathematics
- Combinatorics and probability
Week 9:
- Descriptive Statistics
- Measures of central tendency (mean, median, mode)
- Measures of dispersion (variance, standard deviation)
- Data visualization (histograms, boxplots, scatter plots)
- Inferential Statistics
- Probability distributions (normal, binomial, Poisson, etc.)
- Sampling and sampling distributions
- Hypothesis testing (t-tests, chi-square tests)
Week 10:
- Correlation and Regression Analysis
- Correlation coefficients (Pearson, Spearman)
- Simple linear regression
- Multiple linear regression
- Statistical Inference
- Confidence intervals
- Analysis of variance (ANOVA)
Week 11: Overview of machine learning concepts
- Types of machine learning: supervised, unsupervised, reinforcement learning
- Applications of machine learning in various industries
Week 12: Introduction to linear regression
- Simple linear regression: modeling relationships
- Multiple linear regression: handling multiple variables
Week 13:
- Model evaluation and metrics - Mean squared error (MSE), root mean squared error (RMSE)
- Coefficient of determination (R-squared)
- Techniques for improving regression models
- Feature selection and engineering
- Regularization methods (Lasso, Ridge)
Week 14:
- Introduction to logistic regression
- Understanding binary and multi-class classification problems
- Model interpretation and evaluation
- Confusion matrix and classification metrics (accuracy, precision, recall, F1-score)
- ROC curve and area under the curve (AUC)
Week 15: Practical implementation
- Building logistic regression models using Python
Week 16: Datatype, Absolute & Relative reference, Aggregation functions - Sum, max, min, count, average etc, CountA, CountBlank, Date and time related functions - DatedIF, Network Days, Workdays
Week 17: Logical functions like If else, Ifs, And, OR, Not
Conditional Formatting - Basic, Custom, Advanced - Named Range and Table range
Week 18 : Pivot table, Dashboard , Power Query & Power Pivot, Introduction to Data Modelling
Week 19 : Look up functions - VLookup, HLookup, Index, Match, Offset, Indirect , What if Analysis , Solver
Week 20: Introduction to BI and Power BI , Bar Charts, Pie Charts, Donut Chart, Funnel Chart, Ribbon Chart, Line Chart, Area Chart , Combo Chart, Scatter, Waterfall, Treemap Charts , Maps, Filled Maps
Week 21: Tables, Conditional Formatting, Matrix , Gauge Chart, Multirow Cards, Filter, Drill, Slicer, Animated Bar Chart, Word Cloud, Sunburst , Plyaxis, Scroller, Infographics , Inserting Objects in Power BI: Text, Image, Shapes
Week 22: Creating Reports in Power BI, Publishing in Power BI , Power BI Dashboard, How to Refresh Data in Power BI , Power Query Introduction , Adding/Removing Rows, Text Add-Columns-Transform Column
Week 23: Number Function, From Date Add Column Transform Column , Appending Sheets, Merging Sheets, Columns from Example, Conditional Column , Fill, GroupBy, Transpose , Keep and Remove Columns and Rows , Importing Dataset to SQL and Connecting to Power BI
Week 24: Creating and Deleting Relationships , Normalization 1NF, 2NF, 3NF , Denormalization, OLTP vs OLAP , Data Types and Operators , Intro to DAX, Date Function, Text Function, Logical Function, Introduction to M Language and List Tools , Custom Tools in M Language, Creating Your Own Query in M Language
Week 25: How to Use Python Script in Power BI , How to Use R Script in Power BI , Importing Data from SQL , Importing Data from OData , Connecting from Web Data , Intro to RLS , Static RLS, Dynamic RLS , Enhanced RLS , RLS with Manager Access
Week 26: Introduction to databases, ACID Properties, Data types in SQL, MYSQL Workbench, DDL, DML, Constraints, Where Clause, Group By clause, Having Clause, Order by, Top, Limit
Week 27: Subqueries, Joins(Inner join, Left outer join, Right outer join, Full outer join, self join, Cross join), String transformation, Date time manipulation, Case When Statements, Common Table Expressions (CTE & Recursive CTE)
Week 28: Window Functions(Aggregate Functions, Row number, Rank, Dense Rank, Percent Rank, Ntile, Lag, Lead, First value, Last value, nth_value), Stored Procedures, TCL(Commit, Rollback, Savepoint), Views
Week 29:
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.
Registration Extended Till 7th July
+ 18% GST
I have transitioned my career from Manual Tester to Data Scientist by upskilling myself on my own from various online resources and doing lots of Hands-on practice. For internal switch I sent around 150 mails to different project managers, interviewed in 20 and got selected in 10 projects.When it came to changing company I put papers with NO offers in hand. And in the notice period I struggled to get a job. First 2 months were very difficult but in the last month things started changing miraculously.I attended 40+ interviews in span of 3 months with the help of Naukri and LinkedIn profile Optimizations and got offer by 8 companies.
Based on my career transition and industrial experience, I have designed this course so anyone from any background can learn Data Science and become Job-Ready at affordable price.
Fastest Career Growth
Attractive Compensation
Increasing Demand
Versatility Across Industries
Impact Businesses Directly
Suitable even for Non-Tech People
