Description
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
Introduction to the course
Introduction to Data Science
Introduction to Analytics
Design Thinking and Problem Statement
Mini Project 1
Project 1
Master Python Programming
Python Basics
Python Variables: int, float, string, bool, complex
Conditional statements
Loops
Python Collections: List, Tuple, Dictionary, Set, Frozenset
Mini Project 3
Functions and Methods
Class & Objects
Mini Project 4
Numpy
Working with CSV and Text files
Working with Database
Error Handling
Regular Expression
Project 2: Using Python concepts
Descriptive Statistics
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
Data Visualization
Story Telling
Scipy
Pandas
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
Web Scrapping
Project 4: Web scraping and visualization
Inferential Statistics
Probability
Discrete Probability Distribution: Binomial Distribution, Poisson
Continuous Probability Distribution: Normal, t distribution, Exponential
Correlation
Mini Project 11
SQL Programming
Introduction to Database
Introduction to SQL
SQL JOIN and OPERATORS
CRUD operations on Tables
Data Wrangling with SQL
Project 5
R programming
R Basics
Data types
Loops
Data Visualization
Regression: Simple and Multiple
Classification: KNN, Logistics
Clustering: K Means, Hierarchical
Project 6
Statistics
Probability
Probability Distribution – Discrete and Continuous
Hypothesis building
Data Science Methodology
Introduction
Types of learning
Data Acquisition
Data Wrangling
Model Development
Model Evaluation
Scikit-Learn package
Machine Learning
Regression: Simple linear, multiple linear, ridge, lasso, decision tree, random forest
Project 7
Classification: svm, decision tree, random forest, naïve bayes, bagging, boosting
Project 8
Clustering: K means, Hierarchical
Project 9
Association: Market Basket Analysis
Mini Project 12
Deep Learning
Neural Network – ANN, CNN, RNN
Autoencoders
Long Short-term memory (LSTM)
Restricted Boltzman Machine (RBM)
Project 10
Working with Tableau
Introduction to Visualization
Concepts: Filter, Join, Hierarchy, Groups, Set
Charts and Dashboard
Forecasting and Clustering in Tableau
Business Stories
Duration & Timings :
Total Hours – 100 – 120 Hours.
Training Type: Online Live Interactive Session.
Faculty: Experienced.
Access to Class Recordings.
Weekend Morning Schedule:
Start Date :Monday, October 2, 2023.
Duration: 13 – 15 Weeks
Days: MON – THU (4 Days/ Week)
Time – 8:30 PM to 10:30 PM (EST)
Inquiry Now
USA: +1 734 418 2465 | India: +91 40 4018 1306
+91 40 4018 1306
+1 734 418 2465
info@learntek.org
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