Descriptions

Most entry-level data analyst positions require appetite for data. Effective data analysis helps organizations make business decisions. Nowadays, data is collected by businesses constantly: through surveys, online monitoring, online marketing analyses, collected subscription and registration data (e.g. newsletters), social media monitoring, among other things. Through hands-on projects, students will learn how to apply these techniques to real-world data sets and gain the skills necessary to make data-driven decisions. The course is suitable for beginners who are interested in pursuing a career in data analytics or for professionals looking to enhance their data analysis skills. By the end of the course, students will have a solid foundation in data analytics and be able to use these skills to inform business decisions.

Course Content:

• Introduction to Python & ML
• Use of ML and Python in Software Industry

1.Introduction

• Perspective of Python
• Class & Objects
• Installing Anaconda
• Keywords
• Identifiers
• Datatype

2.Operation & Control Flow

• Arithmetic Operators
• Increment or Decrement Operator
• Relational Operators
• Equality Operators
• Logical operators
• Assignment Operators
• Lambda

3.Data handling and Visualization

• NumPy
• Pandas
• Matplotlib

4.Linear Algebra

• Point
• Line
• Plane
• Hyper Plane
• Geometric Shape as a classifier

5.Distance

• Euclidian
• Angular
• Directed
• Cosine

6.Statistics

• Mean
• Median
• Mode
• Population and Sample
• Gaussian Distribution
• CDF & PDF
• Confidence Interval
• Chebyshev’s inequality
• Co-Variance
• Pearson Correlation Coefficient
• Spearman Rank Correlation Coefficient

7.PCA

• Why PCA
• Eigen Value and Eigen Vector
• MNIST dataset Visualization

8.Linear Regression

• Model (Price Prediction)
• Logistic Regression

9.Optimization Techniques

• Gradient Descent
• Stochastic gradient descent
• Ada Boosting

10.KNN

• KNN
• Geometric Meaning of KNN
• Model (Flower Species Dataset)
• Various Conditions and How to handle the situation

11.Naive Bayes algorithm

• Math behind the Naïve Bayes
• Model (Flower Species Dataset)

12.Decision Tree

• Decision Tree and Decision Forest
• How Decision Tree work
• Model

13.Unsupervised Learning

• What is Unsupervised Learning

14.K Means

• K Means
• K Means ++

15.dB-scan

• DB scan
• Math and logic behind DB-scan
• Implementation area in industries

16.Algometric Clustering

• Algometric Clustering

17.Collaborative Filtering

• Collaborative Filtering

18.Excel

• Excel Formulas
• Advance Formulas like Vlookup, index match
• Play with Chart
• Optimization in Excel

19.Power BI

• Data Types
• Chart
• Auto Filtering
• if else condition
• Adding columns
• Data Modeling
• DAX
• Dashboard Formatting

20.SQL

• SQL Syntax
• SQL Data Types
• SQL Operators
• SQL Expressions
• SQL Clauses
• SQL Queries and Subqueries
• SQL Joins
• String Handling
• Report Automation using python and SqlvaiGmail(Automatic report generation and delivery).
• Practice exercise on Hacker rank

With one Live Project