About the course:
Our data analyst certification helps you learn analytics tools and techniques, how to work with SQL databases, R and Python, how to create data visualizations, and apply statistics and predictive analytics in a business environment
Skills you will learn:
Python Programming, R Programming, SQL Programming, Tableau, Statistics for Machine Learning, understanding of data structure and data manipulation, machine learning model building, deep learning
The following topics will be covered as part of Data Analytics Mentorship Program .
Introduction to the course
Introduction to Data Science
Introduction to Analytics
Design Thinking and Problem Statement
Mini Project 1
Master Python Programming
Python Variables: int, float, string, bool, complex
Python Collections: List, Tuple, Dictionary, Set, Frozenset
Mini Project 3
Functions and Methods
Class & Objects
Mini Project 4
Working with CSV and Text files
Working with Database
Project 2: Using Python concepts
Data and Types
Central Tendency: Mean, Median, Mode
Deviation: Range, Variance, Standard Deviation
BoxPlot and its importance
Mini Project 5
Frequency distribution and its importance
Mini Project 6
Scatter Plots and its importance
Mini Project 7
Mini Project 8
Matplotlib: basic plots and advanced plots
Mini Project 9
Seaborn: basic plots and advanced plots
Mini Project 10
NLP: N-gram models of language
Project 3: NLP
Project 4: Web scraping and visualization
Discrete Probability Distribution: Binomial Distribution, Poisson
Continuous Probability Distribution: Normal, t distribution, Exponential
Mini Project 11
Introduction to Database
Introduction to SQL
SQL JOIN and OPERATORS
CRUD operations on Tables
Data Wrangling with SQL
Regression: Simple and Multiple
Classification: KNN, Logistics
Clustering: K Means, Hierarchical
Probability Distribution – Discrete and Continuous
Data Science Methodology
Types of learning
Regression: Simple linear, multiple linear, ridge, lasso, decision tree, random forest
Classification: svm, decision tree, random forest, naïve bayes, bagging, boosting
Clustering: K means, Hierarchical
Association: Market Basket Analysis
Mini Project 12
Neural Network – ANN, CNN, RNN
Long Short-term memory (LSTM)
Restricted Boltzman Machine (RBM)
Working with Tableau
Introduction to Visualization
Concepts: Filter, Join, Hierarchy, Groups, Set
Charts and Dashboard
Forecasting and Clustering in Tableau
Duration & Timings :
Duration – 100 Hours.
Training Type: Online Live Interactive Session.
Access to Class Recordings.
Start Date : Saturday, April 1, 2023.
Timing – 9:30 AM – 12:30 PM EST.
Days: SAT & SUN (2 Days / Week).
Duration – 16 Weeks.
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