Data Science

Become a Data Scientist in 60 Hours

Data Science is the fastest-growing and most in demand IT field nowadays.

Data Science can be applied to any field, hence the abundance of jobs.

Easiest way to get an IT job.

>>>>>>>Maximize Success<<<<<<<

Data Science

What This Course Offers

In this course you will learn to code using Python, R language and SAS for effective data analysis. Apart from installing and configuring software necessary for statistical and programming environment, this course includes programming concepts, reading, writing of code, debugging and profiling. Each topic will be accompanied by examples.

Who Should Attend

• Anyone from any domain as no hard core programming knowledge is required
• Professionals who wish to switch to IT. Data Science is high in demand and easiest way to get into IT
• wish to explore their interest in software industry
• Are already affiliated to IT and wish to upgrade their skills
• wish to eventually steer their learning towards a related field

What You Get

• Course material
• Robust portfolio of learning material to guide you at work
• Homework
• Assignments
• Projects
• Certificate of completion
• Portfolio reviews
• Resume support
• Mock interviews
• Career coaching

Duration

60 Hours

Learning Modes

• Classroom
• Online
• One on one private classes

Schedule

New session starts soon. Call 470-336-3966 or email contact@redbeartechnologies.com for details

Syllabus

Business Statistics Essentials

• Data Types
• Descriptive Statistics
• Sampling
• Data Distributions
• Inferential Statistics
• Hypothesis Testing

R Language Essentials

• Introduction
• Object Types
• Variables
• Decision Statements and Conditional Loops
• String manipulations
• Sub setting data
• Casting and melting data
• Merging data sets

Exploratory Data Analysis and Visualization

• Getting data into R
• Cleaning and preparing data
• Handling missing values
• ggplot2 for visualization
• geom(), dodge etc for adding dimensions
• Introduction to Tableau for visualization
• Correlation
• Spurious correlation
• Correlation vs. causation

Predictive Analysis

• Types
• Supervised Learning
• Classification

Decision Trees

• Classification and Regression Tees (CART)
• K nearest neighbors (KNN)
• Re-Sampling and Ensembles Methods
• Advanced Methods
• Probabilistic Methods
• Un-Supervised Learning
• Principal component analysis
• Forecasting
• Market basket analysis
• Text analysis

Model Validation and Deployment

• Error measurement
• Cross validation
• Batch vs. real-time scoring