DataScience
About the 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?
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.
What will you be learning?
Within this course, you can get the opportunity to work inside a data science project. During each step, you will be learning different aspects of data science from data analyzing to its visualization and communication. Through the course, learners will be exposed to essential skills required to become a data scientist.
This course will survey and discuss the following topics of data science in detail:
 Data manipulation
 Working with big data
 Data analysis
 Statistics and machine learning
 Data communication
 Information visualization
Along with this, the learners will know the depth and breadth of data science usage in the modern world. From calculating network’s speed to storing the employer’s details, data is being used and stored everywhere and hence data science is a must requisite especially for of IT professionals.
What are the prerequisites for taking up this course?
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 prerequisites you can always take up other relevant introductory courses provided by ABC learn.
DataScience Classroom Timing
Jun MonFri 
7:30 AM  9:30 AM ( IST ) 


DataScience Course Syllabus
 What is data science and why is it important?
 Prerequisites
 Prospects
 Data science tools & technologies
 What is Machine Learning
 Why Machine Learning in Data Science
 Applications
 Types of data
 Raw data handling
 Processed or transformed data
 Decision making from data
 Statistics: Making sense of data
 Uni Variate analysis
 Measure of central tendency
 Mean–Median – Mode
 Range
 IQR
 Variance
 Standard deviation
 Correlation
 Probability Theory
 Uni Variate analysis
 Measure of central tendency
 Mean–Median – Mode
 Range
 IQR
 Variance
 Standard deviation
 Correlation
 Probability Theory
 Skewness
 Kurtosis
 Gaussian distribution
 Multivariate Gaussian distributions
 Binomial distributions
 Poisson distributions
 Supervised learning – regression, classification
 Unsupervised learning  clustering
 Reinforcement learning
 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
 Creating a Dictionary
 Accessing Values in Dictionary:
 Updating Dictionary
 Delete Dictionary Elements
 Properties of Dictionary Keys
 Creating a Tuples
 Accessing Values in Tuples:
 Updating Tuples
 Delete Tuple Elements
 Basic Tuples Operations
 Creating a list
 Accessing Values in Lists
 Updating Lists
 Delete List Elements
 Numpy array
 Array manipulation
 Array mathematics
 Array operations
 Vector and Matrix
 Broadcasting
 Introduction to series and data frames
 Pandas for Data Wrangling
 Overview
 Reading data
 Exploration
 GroupBy
 Indexing
 Hands on implementations
 Why missing value treatment is required?
 Why data has missing values?
 Methods to treat missing value
 Hands on implementations
 What is an outlier?
 What are the causes of outliers?
 What is the impact of outliers on dataset?
 Detecting outliers
 Treating outliers
 Linear regression
 Hypothesis
 Gradient Descent
 Prediction
 Normalization
 Hands on implementations
 Logistic regression
 Sigmoid function
 Decision Boundary
 Confusion matrix
 Hands on implementations
 What is feature engineering?
 What is the process of feature engineering?
 What is variable transformation?
 Mean Squared Error
 K fold cross validation
 Accuracy, Precision, Recall
 Hands on implementations
 Time Series variables
 Components of Time Series data
 Models for time series forecasting
 Exponential smoothing models
 Cross validation for time series data
 Hands on implementations
 Introduction to PCA
 PCA run with Unscaled and scaled predictors).
 Implement PCA
 Hands on implementations
 What is a decision tree?
 Decision tree algorithms
 How does it work?
 Implementation
 Hands on implementations
 What is random forest?
 Advantages of random forest
 Disadvantages of random forest
 Random forest implementation
 Hands on implementations
 What is KNN algorithm?
 How to select appropriate k value?
 Calculating distance
 KNN algorithm – pros and cons
 Hands on implementations
 Why clustering?
 K means clustering
 Number of clusters k=?
 Hierarchical Clustering
 DBSCAN Clustering
 Performance evaluation
 Pros and cons
 Hands on implementations
 Overview
 Classification Using a Separating Hyperplane
 The Maximal Margin Classifier ?Nonseparable Case
 Support Vector Classifiers  Details
 Support Vector Machines  Classification with nonlinear boundaries
 Hands on implementations
 Introductory Concepts
 Feed Forward Neural Networks
 Multilayer Feed Forward Neural Networks
 Motivation and formulation
 Learning Algorithm
 Backpropagation
 Convergence and Optimization
 Loss functions
 Limitations
 Applications
 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 implementations
 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 implementations
 Choose your own algorithm
 Final Project
 Further study
 Next steps in your career
 Linear regression
 Hypothesis
 Gradient Descent
 Prediction
 Normalization
 Hands on implementations
 Logistic regression
 Sigmoid function
 Decision Boundary
 Confusion matrix
 Hands on implementations
Learn DataScience With The Best Institute In Hyderabad
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.