← Data Science & Analytics

Fees by level

Start at any level, or take the complete programme. The fee you pay for a level is locked for you.

All fees are in Indian Rupees and include applicable taxes. See our pricing & payment terms.

The complete curriculum

01 Business Analytics 1 lesson ▶

1. 1.1Introduction to Business Analytics

02 Installation Videos 3 lessons ▶

1. 2.1Anaconda Installation
2. 2.2MySQL Installation
3. 2.3PowerBI Installation

03 Introduction to Spreadsheets, Data and Analysis 1 lesson ▶

1. 3.1Introduction to Spreadsheets, Data and Analysis

04 Data Preparation Techniques 4 lessons ▶

1. 4.1Cell Referencing
2. 4.2QA
3. 4.3Data Filtering
4. 4.4Find and Replace, Remove Duplicates, Missing Data, Data Validation

05 Functions 5 lessons ▶

1. 5.1Count Functions
2. 5.2Aggregate Functions
3. 5.3String Functions
4. 5.4DateTime Functions
5. 5.5Find, Replace, Substitute

06 Advanced Functions 5 lessons ▶

1. 6.1Conditional Functions
2. 6.2Scenario
3. 6.3Conditional Aggregation Functions
4. 6.4Conditional Formatting Techniques
5. 6.5Match and Index Functions

07 Lookups 1 lesson ▶

1. 7.1Lookup Functions (VLookup, HLookup, XLookup)

08 Pivot Tables and Charts 3 lessons ▶

1. 8.1Pivot Tables
2. 8.2Charts
3. 8.3Surprise Q

09 What If Analysis 3 lessons ▶

1. 9.1Goal Seek
2. 9.2Scenario Manager
3. 9.3Data Table

10 Data Analysis in Excel 1 lesson ▶

1. 10.1Data Analysis

11 DBMS 1 lesson ▶

1. 11.1DBMS

12 SQL Commands 2 lessons ▶

1. 12.1SQL Basics
2. 12.2SQL Commands

13 SQL Basics 1 lesson ▶

1. 13.1SQL Basics

14 Aggregate Functions 1 lesson ▶

1. 14.1Aggregate Functions

15 Joins 3 lessons ▶

1. 15.1What is Joins
2. 15.2Type of Joins
3. 15.3Joins Questions

16 Case Statements 1 lesson ▶

1. 16.1Case Statements

17 Date Functions 1 lesson ▶

1. 17.1Date Functions

18 Set Operators 1 lesson ▶

1. 18.1Set Operators

19 Sub-Query 1 lesson ▶

1. 19.1Sub-Query

20 CTE & View 1 lesson ▶

1. 20.1CTE & View

21 Window Functions 2 lessons ▶

1. 21.1Window Functions Part 1
2. 21.2Window Functions Part 2

22 Project 5 lessons ▶

1. 22.1Project_1
2. 22.2Project_2
3. 22.3Project_3
4. 22.4Project_4
5. 22.5Project

23 Introduction to BI 1 lesson ▶

1. 23.1Introduction to Power BI

24 Power Query Editor 4 lessons ▶

1. 24.1Data Transformation + Data Types
2. 24.2Replace Text + Extract + Split
3. 24.3Trim + Clean
4. 24.4Assignment

25 Data Modelling 1 lesson ▶

1. 25.1Data Modelling

26 Data Visualization 13 lessons ▶

1. 26.1Visualization
2. 26.2ETL
3. 26.3Column Chart
4. 26.4Pie Chart
5. 26.5Line Chart
6. 26.6Table
7. 26.7Slicers
8. 26.8Filters
9. 26.9Tree Visualization
10. 26.10Introduction
11. 26.11Univariate Analysis
12. 26.12Bivariate Analysis
13. 26.13Multivariate Analysis

27 DAX 3 lessons ▶

1. 27.1Logical DAX
2. 27.2Text DAX
3. 27.3DateTime DAX

28 Business Cases with Advanced DAX 7 lessons ▶

1. 28.1Problem-1
2. 28.2Problem-2
3. 28.3Problem-3
4. 28.4Problem-4 Extended
5. 28.5Problem-4
6. 28.6Problem-5
7. 28.7Problem-6

29 Advanced PowerBI Features 4 lessons ▶

1. 29.1Bookmarks
2. 29.2RLS
3. 29.3Field Parameters
4. 29.4AI Visuals + Quick Measures

30 Introduction_To_Python 1 lesson ▶

1. 30.1Introduction_To_Python

31 Python Variables 1 lesson ▶

1. 31.1Python Variables

32 Python Data Types 1 lesson ▶

1. 32.1Python Data Types

33 Python Operators 1 lesson ▶

1. 33.1Python Operators

34 Python Strings 2 lessons ▶

1. 34.1Python Strings-1
2. 34.2Python Strings-2

35 Python Conditional Statements 1 lesson ▶

1. 35.1Python Conditional Statements

36 Python Loops 1 lesson ▶

1. 36.1Python Loops

37 Python Data Structures 6 lessons ▶

1. 37.1Data Structures
2. 37.2List
3. 37.3Tuple
4. 37.4Set
5. 37.5Dictionary
6. 37.6Comprehension

38 Functions in Python 4 lessons ▶

1. 38.1Basics of Functions
2. 38.2Lambda Functions
3. 38.3Special Functions
4. 38.4In-Built Function and Recursion

39 OOPs in Python 2 lessons ▶

1. 39.1Oops
2. 39.2Pillars of OOPs

40 Probability 4 lessons ▶

1. 40.1Intro to Probablity
2. 40.2Probability
3. 40.3Permutation
4. 40.4Combination

41 Baye's Theorem 1 lesson ▶

1. 41.1Baye_s Theorem

42 Discrete Probability (Probability Mass Function) 1 lesson ▶

1. 42.1Discrete Probability

43 Continuous Probability (Probability Dist Function) 1 lesson ▶

1. 43.1Continuous Probability

44 Central Limit Theorem 1 lesson ▶

1. 44.1Central Limit Theorem

45 Hypothesis Testing 6 lessons ▶

1. 45.1Hypothesis Testing
2. 45.2Establish Hypothesis + Tests
3. 45.3Hypothesis Testing - Z,T Test
4. 45.4Hypothesis Testing - ANOVA
5. 45.5Hypothesis Testing - KS Test
6. 45.6Hypothesis Testing - Chi Square Test

46 AB Testing 1 lesson ▶

1. 46.1AB Testing

47 Guestimates 8 lessons ▶

1. 47.1Gustimates Overview
2. 47.2Guestimate in detail + Problem-1
3. 47.3Problem-2
4. 47.4Problem-3
5. 47.5Problem-4
6. 47.6Problem-5
7. 47.7Problem-6
8. 47.8Problem-7

48 Introduction To ML 1 lesson ▶

1. 48.1Introduction To ML

49 Framing an ML Problem 1 lesson ▶

1. 49.1Framing an ML Problem

50 EDA 6 lessons ▶

1. 50.1EDA Theory
2. 50.2Understanding Data
3. 50.3Univariate Analysis
4. 50.4Bivariate Analysis
5. 50.5Multivariate Analysis
6. 50.6Sweetviz

51 Folder Problem 1 lesson ▶

1. 51.1Folder Problem

52 SQL+PowerBI 1 lesson ▶

1. 52.1SQL+PowerBI

53 NumPy Basics 7 lessons ▶

1. 53.1Creating Numpy Arrays
2. 53.2Attributes of a NumPy Array
3. 53.3Creating NumPy of Different Dimensions
4. 53.4Accessing Elements
5. 53.5Why NumPy
6. 53.6Case-Study-1
7. 53.7Case-Study-2

54 NumPy Operations 11 lessons ▶

1. 54.1Comparison Operators
2. 54.2Mathematical Operators
3. 54.3Statistical Operators
4. 54.4Sorting Operators
5. 54.5Combining Operators
6. 54.6Manipulation Operators
7. 54.7Random Operators
8. 54.8Miscellaneous Opeartors
9. 54.9Case Study 1
10. 54.10Case Study 2
11. 54.11Correction

55 Pandas Basics 5 lessons ▶

1. 55.1Pandas Basics
2. 55.2Pandas Data Structures
3. 55.3Column Operations
4. 55.4Row Operation
5. 55.5Case Study

56 Pandas Analysis 12 lessons ▶

1. 56.1Accessing specific rows
2. 56.2Miscellaneous functions
3. 56.3String functions
4. 56.4Case Study
5. 56.5Group By
6. 56.6Pivot Table
7. 56.7Group By vs Pivot Table
8. 56.8Case Study - 2
9. 56.9Melt
10. 56.10CrossTab
11. 56.11Pandas Cut
12. 56.12Date Time Function

57 Pandas Merge and Concat 2 lessons ▶

1. 57.1Concat
2. 57.2Merge

58 Missing Values 5 lessons ▶

1. 58.1None vs Nan
2. 58.2isnull vs isna
3. 58.3Treating Missing Values
4. 58.4Apply vs Transform
5. 58.5Case Study

59 Matplotlib 6 lessons ▶

1. 59.1Matplotlib - Line Plot
2. 59.2Matplotlib - Bar Plot
3. 59.3Matplotlib - Pie Plot
4. 59.4Matplotlib - Histograms
5. 59.5Matplotlib - Scatter Plot
6. 59.6Matplotlib - Sub Plots

60 Seaborn 7 lessons ▶

1. 60.1Introduction
2. 60.2Seaborn - Line Plot
3. 60.3Seaborn - Bar Plot
4. 60.4Seaborn - Count Plot vs Histogram vs Distplot
5. 60.5Seaborn - BoxPlot
6. 60.6Seaborn - Vioin Plot
7. 60.7Seaborn - Scatter Plot

61 Plotly 5 lessons ▶

1. 61.1Plotly - Line Plot
2. 61.2Plotly - Area Plot
3. 61.3Plotly -Scatter Plot
4. 61.4Plotly - Box + Violin Plot
5. 61.5Plotly - 3-D Plot

62 Data Cleansing 5 lessons ▶

1. 62.1Data Cleansing Theory
2. 62.2Data Cleansing Practical - 1
3. 62.3Data Cleansing Practical - 2
4. 62.4Data Cleansing Practical - 3
5. 62.5Data Cleansing Practical - 4

63 Treating Missing Values 1 lesson ▶

1. 63.1Treating Missing Values

64 Outlier Detection 3 lessons ▶

1. 64.1Outlier Detection Theory
2. 64.2Z-Score based Filter
3. 64.3IQR based Filter

65 Feature Construction 1 lesson ▶

1. 65.1Feature Construction

66 Feature Selection 6 lessons ▶

1. 66.1Correlation
2. 66.2Mutual Information Score
3. 66.3Variance Inflation Factor
4. 66.4ANOVA
5. 66.5Chi-Square
6. 66.6Recursive Feature Elimination

67 Feature Extraction 1 lesson ▶

1. 67.1Feature Extraction

68 All Supervised Models 2 lessons ▶

1. 68.1Types of ML
2. 68.2All Supervised Models

69 Linear Regression 9 lessons ▶

1. 69.1Simple LR Theory
2. 69.2Assumptions for LR
3. 69.3Evaluation Metrics for Regression
4. 69.4Types of LR
5. 69.5LR Practical
6. 69.6Gradient Descent
7. 69.7Bias Variance Tradeoff
8. 69.8Regularization Theory
9. 69.9Regularization Practical

70 Logistic Regression 5 lessons ▶

1. 70.1Logistic Regression Theory
2. 70.2Logistic Regression Practical
3. 70.3Evaluation Metrics for Classification Theory
4. 70.4Evaluation Metrics for Classification Practical
5. 70.5Imbalanced Data

71 Decision Tree 2 lessons ▶

1. 71.1Decision Tree Theory
2. 71.2Decision Tree Practical

72 Ensemble Techniques 7 lessons ▶

1. 72.1Introduction to Ensemble Techniques
2. 72.2Random Forest Theory
3. 72.3Random Forest Practical
4. 72.4Boosting Algorithms Theory
5. 72.5Boosting Algorithms Practical
6. 72.6Stacking Theory
7. 72.7Stacking Practical

73 KNN 2 lessons ▶

1. 73.1KNN Theory
2. 73.2KNN Practical

74 Naive Bayes 2 lessons ▶

1. 74.1Naive Bayes Theory
2. 74.2Naive Bayes Practical

75 SVM 2 lessons ▶

1. 75.1SVM Theory
2. 75.2SVM Practical

76 CCA 2 lessons ▶

1. 76.1CCA Theory
2. 76.2CCA Practical

77 Mean Median Mode 2 lessons ▶

1. 77.1Mean Median Mode Theory
2. 77.2Mean Median Mode Practical

78 Random Sample Imputation 2 lessons ▶

1. 78.1Random Sample Imputation Theory
2. 78.2Random Sample Imputation Practical

79 Missing Indicators 2 lessons ▶

1. 79.1Missing Indicators Theory
2. 79.2Missing Indicators Practical

80 KNN Imputation 2 lessons ▶

1. 80.1KNN Imputation Theory
2. 80.2KNN Imputation Practical

81 MICE 2 lessons ▶

1. 81.1MICE Theory
2. 81.2MICE Practical

82 Handling Categorical Values 3 lessons ▶

1. 82.1Theory
2. 82.2Practical
3. 82.3Case Study

83 Feature Scaling 2 lessons ▶

1. 83.1Theory
2. 83.2Practical

Tools you'll use

Related courses