MiDaS Training Program by Start-Tech Academy

MiDaS Training Program

Comprehensive Data Science training program covering MS Excel, SQL and Machine Learning models in Python.
Below are the contents with few open-for-preview videos.
*Click here and upload student ID to avail at student price (Rs. 5000)

Contents

Microsoft Excel Masterclass
Course Overview
3 mins
Mathematical Formulas
7 mins
Exercise 1/12: Mathematical Formulas
64.3 KB
Study Material and all Exercise files
13.5 MB
Textual Formulas
8 mins
Exercise 2/12: Textual Formulas
66.2 KB
Logical Formulas
12 mins
Exercise 3/12: Logical Formulas
49.2 KB
Date-time (Temporal) Formulas
8 mins
Exercise 4/12: Date-Time Formulas
51.2 KB
Lookup Formulas
9 mins
Exercise 5/12: Lookup Formulas
272 KB
Data Tools
20 mins
Exercise 6/12: Data Tools
849 KB
Formatting data and tables
18 mins
Exercise 7/12: Formatting
13.5 KB
Pivot Tables
9 mins
Exercise 8/12: Pivot tables
174 KB
Charts-Part 1
10 mins
Charts-Part 2
12 mins
Exercise 9/12: Charts
25.8 KB
Excel Shortcuts
13 mins
Exercise 10/12: Shortcuts
8.49 MB
Analytics in Excel
16 mins
Exercise 11/12: Analytics
16.8 KB
Macros
11 mins
Exercise 12/12: Macros
13.6 KB
Waterfall chart
12 mins
Infographics 1: Cool charts
15 mins
Infographics 2: Cool charts
7 mins
SQL for Data Analytics
Course Overview
5 mins
What is SQL
2 mins
Tables and DBMS
4 mins
Types of SQL commands
5 mins
PostgreSQL
3 mins
Installation
7 mins
Study Material and datasets
9.98 MB
Case Study Part 1
5 mins
Case Study Part 2
7 mins
CREATE
12 mins
Exercise 1: Create DB and Table
2 mins
Solutions to all Exercises
734 KB
INSERT
10 mins
Import data from File
5 mins
Solutions to all Exercises
4 mins
Exercise 2: Inserting and Importing
1 min
SELECT statement
4 mins
SELECT DISTINCT
7 mins
WHERE
5 mins
Logical Operators
7 mins
Exercise 3: SELECT & WHERE
2 mins
UPDATE
6 mins
DELETE
5 mins
ALTER
18 mins
Exercise 4: Updating Table
2 mins
Restore and Back-up
8 mins
Exercise 5: Restore and Back-up
1 min
IN
5 mins
BETWEEN
6 mins
LIKE
9 mins
Exercise 6: In, Like & Between
1 min
Side Lecture: Commenting in SQL
2 mins
ORDER BY
8 mins
LIMIT
4 mins
Exercise 7: Sorting
1 min
AS
4 mins
COUNT
6 mins
SUM
4 mins
AVERAGE
3 mins
MIN & MAX
5 mins
Exercise 8: Aggregate functions
2 mins
GROUP BY
12 mins
HAVING
6 mins
Exercise 9: Group By
2 mins
CASE WHEN
6 mins
Introduction to Joins
3 mins
Inner Join
9 mins
Left Join
8 mins
Right Join
7 mins
Full Outer Join
5 mins
Cross Join
5 mins
Except
3 mins
Union
4 mins
Exercise 10: Joins
2 mins
Subqueries
15 mins
Exercise 11: Subqueries
2 mins
VIEWS
8 mins
INDEX
7 mins
Exercise 12: Views
1 min
String Functions: LENGTH
4 mins
String Functions: UPPER & LOWER
3 mins
String Functions: REPLACE
5 mins
String Functions: TRIM
7 mins
String Functions: CONCATENATION
3 mins
String Functions: SUBSTRING
7 mins
String Functions: LIST AGGREGATION
6 mins
Exercise 13: String Functions
3 mins
Mathematical Functions: CEIL & FLOOR
4 mins
Mathematical Functions: RANDOM
6 mins
Mathematical Functions: SETSEED
5 mins
Mathematical Functions: ROUND
3 mins
Mathematical Functions: POWER
3 mins
Exercise 14: Mathematical Functions
2 mins
Date-Time Functions: Current Date and Time
5 mins
Date-Time Functions: AGE
4 mins
Date-Time Functions: EXTRACT
9 mins
Exercise 15: Date-time functions
2 mins
PATTERN (STRING) MATCHING: Basics
8 mins
ADVANCE PATTERN MATCHING (REGULAR EXPRESSIONS)
16 mins
Exercise 16: Pattern Matching
2 mins
Data Type conversion functions: Number to String
11 mins
Data Type conversion functions: String to Number
6 mins
User Access Control
14 mins
Bonus Section: Tablespace
6 mins
Bonus Section: Primary and Foreign Keys
6 mins
Bonus Section: ACID
6 mins
Bonus Section: TRUNCATE
4 mins
Performance tuning tip 1
12 mins
Performance tuning tip 2
2 mins
Performance tuning tip 3
3 mins
Performance tuning tip 4
1 min
Performance tuning tip 5
2 mins
Performance tuning tip 6
3 mins
Performance tuning tip 7
2 mins
Performance tuning tip 8
5 mins
Machine Learning Basics with Python
Welcome to the course!
3 mins
Course contents
6 mins
Types of Data
5 mins
Types of Statistics
3 mins
Describing data Graphically
12 mins
Measures of Centers
8 mins
Measures of Dispersion
5 mins
Setting up Python and Jupyter Notebook: Installing Python
4 mins
Opening Jupyter Notebook
10 mins
Introduction to Jupyter
14 mins
Arithmetic operators in Python: Python Basics
5 mins
Strings in Python: Python Basics
20 mins
Lists, Tuples and Directories: Python Basics
19 mins
Working with Numpy Library of Python
12 mins
Working with Pandas Library of Python
10 mins
Working with Seaborn Library of Python
9 mins
Introduction to Machine Learning
17 mins
Building a Machine Learning Model
9 mins
Data Preprocessing: Gathering Business knowledge
4 mins
Data Exploration
4 mins
The Dataset and the Data Dictionary
8 mins
Course Data set and study material
8.85 MB
Importing Data in Python
7 mins
Univariate analysis and EDD
4 mins
EDD in Python
13 mins
Outlier Treatment
5 mins
Outlier Treatment in Python
15 mins
Missing Value Imputation
4 mins
Missing Value Imputation in Python
5 mins
Seasonality in Data
4 mins
Bi-variate analysis and Variable transformation
17 mins
Variable transformation and deletion in Python
10 mins
Non-usable variables
5 mins
Dummy variable creation: Handling qualitative data
5 mins
Dummy variable creation in Python
6 mins
Correlation Analysis
11 mins
Correlation Analysis in Python
8 mins
Linear Regression: The Problem Statement
2 mins
Basic Equations and Ordinary Least Squares (OLS) method
9 mins
Assessing accuracy of predicted coefficients
15 mins
Assessing Model Accuracy: RSE and R squared
8 mins
Simple Linear Regression in Python
15 mins
Multiple Linear Regression
5 mins
The F - statistic
9 mins
Interpreting results of Categorical variables
6 mins
Multiple Linear Regression in Python
15 mins
Test-train split
10 mins
Bias Variance trade-off
7 mins
Test train split in Python
11 mins
Linear models other than OLS
5 mins
Subset selection techniques
12 mins
Shrinkage methods: Ridge and Lasso
8 mins
Ridge regression and Lasso in Python
24 mins
Advanced Machine Learning with Python
Data Preparation for Classification Models: Data Dictionary
9 mins
Data Import in Python
5 mins
EDD in Python
19 mins
Outlier treatment in Python
10 mins
Missing Value Imputation in Python
5 mins
Variable transformation and Deletion in Python
5 mins
Dummy variable creation in Python
6 mins
Three Classifiers and the problem statement
4 mins
Why can't we use Linear Regression?
5 mins
Logistic Regression
8 mins
Training a Simple Logistic Model in Python
13 mins
ResultS of Simple Logistic Regression
6 mins
Logistic with multiple predictors
3 mins
Training multiple predictor Logistic model in Python
7 mins
Confusion Matrix
4 mins
Making Confusion Matrix in Python
10 mins
Evaluating performance of model
8 mins
Evaluating model performance in Python
3 mins
Linear Discriminant Analysis
10 mins
LDA in Python
3 mins
Test-Train Split
10 mins
Test-Train Split in Python
7 mins
K-Nearest Neighbors classifier
9 mins
K-Nearest Neighbors in Python: Part 1
6 mins
K-Nearest Neighbors in Python: Part 2
7 mins
Understanding the results of classification models
7 mins
Summary of the three models
5 mins
Basics of decision trees
11 mins
Understanding a Regression Tree
11 mins
The stopping criteria for controlling tree growth
4 mins
The Data set for the Course
3 mins
Missing value treatment in Python
6 mins
Missing value treatment in Python
4 mins
Dummy Variable creation in Python
5 mins
Dependent- Independent Data split in Python
5 mins
Test-Train split in Python
7 mins
Creating Decision tree in Python
4 mins
Evaluating model performance in Python
5 mins
Plotting decision tree in Python
5 mins
Pruning a tree
5 mins
Pruning a tree in Python
11 mins
Classification tree
7 mins
The Data set for Classification problem
2 mins
Classification tree in Python : Preprocessing
9 mins
Classification tree in Python : Training
14 mins
Advantages and Disadvantages of Decision Trees
2 mins
Ensemble technique 1 - Bagging
7 mins
Ensemble technique 1 - Bagging in Python
12 mins
Ensemble technique 2 - Random Forests
4 mins
Ensemble technique 2 - Random Forests in Python
7 mins
Using Grid Search in Python
13 mins
Boosting
8 mins
Ensemble technique 3a - Boosting in Python
6 mins
Ensemble technique 3b - AdaBoost in Python
4 mins
Ensemble technique 3c - XGBoost in Python
12 mins
Introduction to Support Vector Machines
2 mins
The Concept of a Hyperplane
5 mins
Maximum Margin Classifier
4 mins
Limitations of Maximum Margin Classifier
3 mins
Support Vector classifiers
10 mins
Limitations of Support Vector Classifiers
2 mins
Kernel Based Support Vector Machines
7 mins
Regression and Classification Models
1 min
Importing data for regression model
6 mins
Missing value treatment
4 mins
Dummy Variable creation
5 mins
X-y Split
5 mins
Test-Train Split
7 mins
Standardizing the data
7 mins
SVM based Regression Model in Python
11 mins
Classification model - Standardizing the data
2 mins
SVM Based classification model
12 mins
Hyper Parameter Tuning
10 mins
Polynomial Kernel with Hyperparameter Tuning
5 mins
Radial Kernel with Hyperparameter Tuning
7 mins