Data Analyst Course

DATA ANALYST COURSE

Most entry-level data analyst positions require an 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), and 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 to make data-driven decisions. The course is suitable for beginners 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 FEATURES

  • Course Duration: 8-10 Weeks (40 Hours approx.)
  • Category:Databases, Predictive Analysis
  • Available Modes: Online (Batch or One on One)
  • Certificate: Yes
  • Location: Online – Live Sessions
  • Language: English
  • Sessions: Weekday and Weekend
  •  Prerequisites: No
  • Skill Level: Beginner
  • Course Capacity: 20

Course Contents:

1. Introduction
  • Perspective of Python
  • Class & Objects
  • Installing Anaconda
  • Keywords
  • Identifiers
  • Datatype
  • Types of Organizations. What is a company /client /stakeholder? Structure

  • Decision making, and the communication process of a company/client/stakeholder
  • Structure of IT Project team.
  • Positioning of a Business analyst.
  • NumPy
  • Pandas
  • Matplotlib
  • Point
  • Line
  • Plane
  • Hyper Plane
  • Geometric Shape as a classifier
  • Euclidian
  • Angular
  • Directed
  • Cosine
  • Mean
  • Median
  • Mode
  • Population and Sample
  • Gaussian Distribution
  • CDF & PDF
  • Confidence Interval
  • Chebyshev’s inequality
  • Co-Variance
  • Pearson Correlation Coefficient
  • Spearman Rank Correlation Coefficient
  • Why PCA
  • Eigen Value and Eigen Vector
  • MNIST dataset Visualization
  • Model (Price Prediction)
  • Logistic Regression
  • Gradient Descent
  • Stochastic gradient descent
  • Ada Boosting
  • KNN
  • Geometric Meaning of KNN
  • Model (Flower Species Dataset)
  • Various Conditions and How to handle the situation
  • Math behind the Naïve Bayes
  • Model (Flower Species Dataset)
  • Decision Tree and Decision Forest
  • How Decision Tree work
  • Model
  • What is Unsupervised Learning
  • K Means
  • K Means ++
  • DB scan
  • Math and logic behind DB-scan
  • Implementation area in industries
  • Algometric Clustering
  • Collaborative Filtering
  • Excel Formulas
  • Advance Formulas like Vlookup, index match
  • Play with Chart
  • Optimization in Excel
  • Data Types
  • Chart
  • Auto Filtering
  • if else condition
  • Adding columns
  • Data Modeling
  • DAX
  • Dashboard Formatting
  • 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