Description
What is Data Science?
Data science is a field of providing meaningful information based on large amounts of complex data. Data science, or data-driven science, combines different fields of work in statistics and computation in order to interpret data for the purpose of decision making.
Why Data Science?
The new-found love for data science in today’s computing world isn’t unjustified. Ranked as the hottest job on offer in the coming years by Harvard Business Review.
The fastest-growing roles are Data Scientists and Advanced Analysts, which are projected to see demand spike by 28% by 2020.
Who is a Data Scientist?
Data Scientists are “Part analyst, Part artist”.
The literal meaning for a data scientist is who practised and acquired a good amount of knowledge in data science course. 😊
Someone who gained knowledge and skills in analytics, computer science, mathematics, statistics, data visualisations and communication as well as business and strategy.
Why so much demand for Data Science?
The below viz explains all about how much demand and shortage of skills is increasing year on year.
The following topics will be covered as part of Data Science with Python Training.
Statistics
Introduction to Statistics
Different Areas of Statistics
Central Tendency
Correlation, Covariance, Collinearity
Hypothesis Testing
ANOVA
Chi-square Test.
Python Essentials – Core
Overview of Python – Starting with Python
Python Packages : Numpy, scify, pandas, scikitlearn, nltk etc
Basic Python Programming – Hands-on/Demo
Advanced Python Programming – Hands-on/Demo
Assignment – 1
Introduction To Data Science
Intoduction to Data Analytics
Applications
DS Use-Cases
Exploratory Data Analysis
Missing Value Analysis
Outlier Analysis
Hands-on Demo – Python
Data Pre-Processing
Variable Importance
Normalization
Sampling
Hands-on Demo – Python
Assignment – 2
Introduction to Machine Learning – Supervised ML
Introduction to Machine Learning
Decision Trees
Random Forests
Linear Regression
Logistic Regression
Hands-on Demo – Python
Advanced Machine Learning – (Supervised + Unsupervided) ML
Visualization
KNN
Naive Bayes
Cluster Analysis
Hands-on Demo – Python
Assignment – 3
Natural Language Processing
Introduction to Text Mining
Text Preprocessing
TF-IDF
Word Cloud
Sentiment Analysis
Hands-on Demo – Python
Time Series
Introduction to Time Series
Time Series Variables
ARIMA Model for Forecasting
Exponential Smoothing Models
Hands-on Demo – Python
Assignment – 4
Case Study
Regression Case Study
Classification Case Study
Time Series Case Study
Assignment – 5
Introduction to Deep Learning
Final Project
Pre-requisites :
Basic statistics knowledge and any computer programming language is preferred.
Duration & Timings :
Duration – 30 Hours.
Training Type: Online Live Interactive Session.
Faculty: Experienced.
Access to Class Recordings.
Weekday Session – Mon – Thu 8:30 PM – 10:30 PM EST– 4 Weeks. December 16, 2019.
Weekend Session – Sat & Sun 9:30 AM to 12:30 PM (EST) – 5 Weeks. January 18, 2020.
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