Description
About the course:
This Data Scientist course aims to accelerate your career in Data Science and provides you with world-class training and skills required to become successful in this field. The Data Scientist course offers extensive training on the most in-demand Data Science and Machine Learning skills with hands-on exposure to key tools and technologies including Python, Web Scraping, NLP, Data Visualization, Statistics, and concepts of Machine Learning and Deep Learning.
Skills you will learn:
Python Programming, 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 Science 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
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
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 5
Classification: svm, decision tree, random forest, naïve bayes, bagging, boosting
Project 6
Clustering: K means, Hierarchical
Project 7
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 7
Duration & Timings :
Total Hours – 100 – 120 Hours.
Training Type: Online Live Interactive Session.
Faculty: Experienced.
Access to Class Recordings.
Weekday Evening Schedule:
Start Date :Monday, January 15,2024.
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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