Key Highlights

Data Science using R & Python Trainingcourse includes:

Job Opportunities

Why should you take Data Science?

What you’ll learn

Solve any data analytics problem using R and Python
Perform Logistic Regression using Cancer remission data set.
Linear regression in python
Data visualization in R-Bar graphs,Stacked bar,grouped bar graph,Line chart for time series data,Box plot to calculate mean, median, min ,max ,3rd quartile and 1st quartile values
Basics of Python,importing sys library,platform,checking the platform,run a code,checking data type,performing calculations,concatenating of strings,conversion of values,working on tuples,
Data mapping and indexing using enumerate,creating dictionaries using enumerate,sorting without order,reversing the list,append the list.
Mathematical on Array using Numpy,decalring the 2D array,3D array,dtype,numpy operations,boolean array
Vector operation with data alignment,creating data frame out of dictionary of equal length list,create Data frame by combining two series,create Data frame from array.
Perform Data exploratory functions,importing and exporting the data sets,Data manipulation
Data manipulation in R-Dply,Filter,multiple filter,mutate,arrange,summarize.......etc

This Data Science course from SMEClabs is best for individuals who are looking to transform their careers. People who have the passion to use the data, analyze, visualize and use it for the betterment of the Business and the society. For those mathematics enthusiasts, who can apply maths in real life and solve complex business challenges. This is specifically ideal for the people who are

Analysts and Software engineers looking for a career shift in the data science stream.
Freshers who want to start the career as we teach from the basics and gradually build up your skills.
Individuals who are graduated and working in the Data Science field and looking to upgrade their careers.

The market for Data Analytics is growing across the world and this strong growth pattern translates into a great opportunity for all the IT Professionals. Our Data Science Training helps you to grab this opportunity and accelerate your career by applying the techniques on different types of Data. It is best suited for:

Developers aspiring to be a ‘Data Scientist’
Analytics Managers who are leading a team of analysts
Business Analysts who want to understand Machine Learning (ML) Techniques
Information Architects who want to gain expertise in Predictive Analytics
‘R’ professionals who wish to work Big Data
Analysts wanting to understand Data Science methodologies

Data Science using R & Python Training Syllabus:

Data Science: The Impact of Statistics

Python: Data Science with Python

Project Activity: Sentiment Analysis on tweets.

Numpy: Machine Learning & Scientific Computing

Pandas: Real-World Data Analysis

SciPy:Machine Learning & Scientific Computing

ProjectActivity: Cryptocurrencyconversionforcryptocurrencytrading(Eg:BTC).

Theano: ML & DL

Scikit-learn: ML & Data Mining

Keras: ML, DL & Neural Networks

TensorFlow: ML, DL & NLP

PyTorch: ML, DL& NLP

Natural Language Processing:NLP

R Programming: Data Science

Java:OOPSL

MongoDB: NoSQL

Cassandra: NoSQL

Hadoop: Hadoop Architecture & HDFS

Hadoop: Distributed Processing MapReduce Framework

Kafka: Distributed Streaming

AWS BIG DATA

Scala: Functional Programming

Apache Spark: Next-Generation Big Data Framework

Introduction to Data Science

➢ Life Cycle of Data Science
➢ Skills required for Data Science
➢ Careers Path in Data Science
➢ Applications of Data Science

Statistics

➢ Relationship between Statistics and Data Science
➢ Introduction to Data
➢ Descriptive Statistics
➢ Inferential Statistics
➢ Random Sampling and Probability Distribution

Python for Data Science

➢ Python programming
➢Python for Exploratory Data Analysis

SQL for Data Science

• Introduction to RDBMS
• Retrieving
• Updating
• Inserting
• Deleting
• Sorting AND Filtering
• Summarizing AND Grouping
• Using Subqueries
• Joining Tables
• Views
• Stored Procedure
• Python Database ConnectionAPI

Deep dive into Machine Learning

➢ Introduction To Machine Learning

NLP & Recommender Systems

• Text preprocessing using Bag of words technique
• TF(Term Frequency)
• IDF(Inverse Document Frequency)
• Normalization
• Vectorization
• NLP with Python