Things to know about DataScience Technologies

  • IBM predicts that demand of Data Scientists will show increment of 28% by year 2020.

  • It is the universal truth that most of the giant firms did effective use of available data which represents the power of Data science and big data.

  • Annual demand for the data scientist, data developers, and data engineers will reach nearly 700,000 openings by 2020.

  • 59% of top industries such as IT, finance, professional services are comprised of Data scientist and data analytics.

  • Big data, Hadoop, machine learning and data mining are the huge opportunity ground a skilled data scientist and analytics.

  • The average salary of Data scientist and Data analytics vary between 114,000$ to 120,000$.

  • The heart of every industry, IT, marketing, product or services is based on the concept of Data science and Analytics.

  • From the budding startups to huge global firms, each requires effective and efficient utilization of data and its user data for expansion which defines the bright scope of Data science career.

  • According to Glassdoor, in 2016 data science was the highest paid field.

  • The concepts of economic, supply and demand chain and core of very business is well defined by Data science and data statics.

  • Google, Amazon, Facebook and their huge user base is enough to define the power of Data science.

  • McKinsey predicts that by 2019, there will be a 50% gap in the supply of data scientists versus demand.

  • The future industry such as artificial intelligence, algorithms consists data science and data analysis as major portion.

DataScience Course

Data Science is a new field where students learn to analyze data in many forms to communicate insights. The course focuses on the introduction and basics of data science that can be used in various fields. In today’s, world, data is being collected in every industry and hence there exist ample opportunities for learners to apply for these jobs in reputed organizations.

Who Should learn

This course is for those who are seriously curious about data. The course is suited to IT professionals who wish to pursue a profession in the fields of economics, statistics or computer science. Students holding degrees in cloud computing can take up this course to add another level of aptitude to their qualification. Students with mathematics or statistics background are normally ones who are interested in this type of courses.

Course Syllabus

Week 1 – Schedule

Introduction to Data Science

  • What is data science and why is it important?
  • Pre-requisites
  • Prospects
  • Data science tools & technologies
  • What is Machine Learning
  • Why Machine Learning in Data Science
  • Applications

Understanding About Data

  • Types of data
  • Raw data handling
  • Processed or transformed data
  • Decision making from data
  • Overview of Big data and it’s applications
  • Big data – tools and technologies
  • Statistics: Making sense of data

Statistics and Probability theory

  • Uni Variate analysis
  • Measure of central tendency
  • Mean – Median – Mode
  • Range
  • IQR
  • Variance
  • Standard deviation
  • Correlation

Probability Distributions

  • Skewness
  • Kurtosis
  • Gaussian distribution
  • Multivariate Gaussian distributions
  • Binomial distributions
  • Poisson distributions

Machine learning Overview

  • Supervised learning – regression, classification
  • Unsupervised learning - clustering
  • Reinforcement learning
Week -1 Practice/Review session
 

Week 2 – Schedule

Basics of python for data analysis

  • Why to learn python for data analysis? R vs Python.
  • Installing python and Jupyter notebook
  • Running simple programs in python
  • Data types
  • Variables
  • Conditional statements
  • Functions
  • Loops
  • Modules
  • File I/O
  • Exception handling
  • Hands on

Dictionary

  • Creating a Dictionary
  • Accessing Values in Dictionary
  • Updating Dictionary
  • Delete Dictionary Elements 
  • Properties of Dictionary Keys

Tuples

  • Creating a Tuples
  • Accessing Values in Tuples:
  • Updating Tuples
  • Delete Tuple Elements
  • Basic Tuples Operations

List

  • Creating a list
  • Accessing Values in Lists
  • Updating Lists
  • Delete List Elements

Exploratory analysis in python using Pandas

  • Introduction to series and data frames

Data Munging in Python using Pandas

  • Pandas for Data Wrangling
  • Overview
  • Reading data
  • Exploration
  • GroupBy
  • Indexing
  • Hands on

Missing Value Treatment

  • Why missing value treatment is required?
  • Why data has missing values?
  • Which are the methods to treat missing value?
  • Hands on
Week -2 Practice/Review session
 

Week 3 – Schedule 

Techniques of Outlier Detection and Treatment

  • What is an outlier?
  • What are the causes of outliers?
  • What is the impact of outliers on dataset?
  • How to detect outlier?
  • How to remove outlier?

Regression

  • Linear regression
  • Hypothesis
  • Gradient Descent
  • Prediction
  • Normalization
  • Hands on
Week -3 Practice/Review session

Week 4 – Schedule

  • Logistic regression
  • Sigmoid function
  • Decision Boundary
  • Hands on

The art of feature engineering

  • What is feature engineering?
  • What is the process of feature engineering?
  • What is variable transformation?

Model evaluations

  • Mean Squared Error
  • K fold cross validation
  • Accuracy, Precision, Recall
  • Hands on 
Week -4 Practice/Review session
 

Week 5 – Schedule

PCA

  • Introduction to PCA
  • PCA run with Unscaled and scaled predictors).
  • Implement PCA

Tree based models

  • What is a decision tree?
  • Decision tree algorithms
  • How does it work?
  • Implementation

Ensemble methods of trees based models

  • Random forest
  • What is random forest?
  • Advantages of random forest
  • Disadvantages of random forest
  • Random forest implementation
Week -5 Practice/Review session
 

Week 6 – Schedule

K – Nearest Neighbor

  • What is KNN algorithm?
  • How to select appropriate k value?
  • Calculating distance
  • KNN algorithm – pros and cons
  • Hands on

Cluster analysis 

  • Why clustering?
  • K means clustering
  • Number of clusters k=?
  • Pros and cons
  • Hands on
Week -6 Practice/Review session
 

Week 7 – Schedule

Support Vector Machines

  • Overview
  • Classification Using a Separating Hyperplane
  • The Maximal Margin Classifier _Non-separable Case
  • Support Vector Classifiers - Details
  • Support Vector Machines - Classification with non-linear boundaries
  • Hands on
Week -7 Practice/Review session
 

Week 8 – Schedule

NLP - Text analytics

  • Intro to Natural language processing and sentiment analysis
  • Python NLTK library
  • Bag of words concept
  • Text Preprocessing
  • Noise Removal
  • Lexicon Normalization
  • Lemmatization
  • Stemming
  • Stop words removal
  • Statistical features
  • TF – IDF
  • Frequency / Density Features
  • Text analysis with real life datasets 
  • Hands on
Week -8 Practice/Review session
 

Week 9 – Schedule

Data visualization

  • How to create a scatter plot?
  • How to create a histogram?
  • How to create a bar chart?
  • How to create a stacked bar chart?
  • How to create a box plot?
  • How to create an area chart?
  • How to create a heat map?
  • How to plot a geographical map?
  • Hands on

Trying everything all together

  • Choose your own algorithm
  • Final Projects
  • Further study
  • Next steps in your career
  • How to promote ourselves in the market

Add on:

  • AWS Cloud environment
  • Big Data architecture flow for data

Pre-requisites

The ideal students who are prepared to take up this high level of course will have the following traits:

  • He should have a background in statistics or mathematics
  • He should have interest in data analysis and management
  • He should have prior programming experience
  • He should have an understanding of different variables like python data structures, function loops, dictionaries and lists etc.

Since this is a course that provides a degree level qualification, previous experience in computer science, descriptive statistics and programming is a must. In case you do not fulfill the pre-requisites you can always take up other relevant introductory courses provided by ABC learn.

Find your convenience schedule

29
MAY

Mon-Fri ( 10 Weeks )

07:30 AM - 09:30 AM ( IST )

32,000  27,200

02
JUNE

Sat-Sun ( 10 Weeks )

10:00 AM - 01:00 PM ( IST )

32,000  27,200

Why Learn DataScience With ABCLearn Technologies

 
 

Real Live Projects

Learn use cases of software industry and companies with our live projects. Become expert by learning analytical tools like R, SAS, Hadoop, Python, Tableau etc.

Experienced Trainers

Practical implementation is now the new bench mark and feel it with our constant hands on live projects and training.

Placement Assistance

Our great career counseling team is always there to help you out in finding the best career for you. Availability of applications, projects and case studies to make you an expert of the industry.

Key Knowledge

Time management is easy with our multiple time schedule for classes. Learn advance concepts like Marketing, Retail Analytics & Machine Learning.

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