Data Analyst

Data Analyst

Course Features

  • Course Duration: 6-8 Weeks (35 Hoursapprox)
  • Category: ,
  • Available Modes: Online (Batch or One on One)
  • Certificate: Yes
  • Location: Online - Live Sessions
  • Language: English
  • Sessions: Weekday and Weekend
  • Viewers: 2978
  • Prerequisites: No
  • Skill Level: Beginner
  • Course Capacity: 20
  • Start Course: To be announced

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

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Andrew Greenberg

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