Data Science Training at Lingampally 

Sunshinetechnosystem is one of the most reputed Data Science Training institutes in Lingampally Chandanagar , Hyderabad that delivers practical and real time training for in depth understanding of data analytics and statistical tools. It follows case study approach and provides 100% placement assistance with real time hands on training for the candidates aspiring career in Data Science.

Sunshinetechnosystem provides qualitative and comprehensive training to the Data Science students. The trainer here teaches from the foundation level to advanced levels of Data Science course and offers interactive class room training as well as online training by considering real time examples by professional and certified Data Scientists. The Data Science course is well structured and is informative for the participants in all the aspects.

What is Data Science?

Data science is an umbrella term in which many scientific methodologies apply such as mathematics, statistic, programming languages etc. It is a data driven science applied to extract facts from raw data with an associative match of data inferences, algorithms development and technology for solving analytically difficult problems.

Data Science Overview

Data science is a challenging and engaging task as it demands more comprehensive understanding of facts. It helps to solve and tackle data science problems in general and requires critical thinking as it is related to problem solving skills.

Data science in real time deals with various tools such as R, python and Excel which are used in drawing graphical representation through which one can make fair decision about the progress and make the further improvements if necessary.

Advantages of Data Science for an Organization

• Empowers the management and officers to make better decisions
• Directs actions based on trends that help to define goals
• Challenges the staff to adopt best practices
• Focus on issues that matter
• Identifies opportunities
• Decision making with quantifiable, data-driven evidence
• Tests these decisions
• Identifies and refines target audiences
• Recruit the right talent for the organization

Who can Learn Data Science Course?

• Freshers/Graduates      • Working professionals   • under graduate diplomats   • Managers   • Data analysts  • Business analysts   • Operators    • Job Seekers   • End users    • Software Developers   • IT professionals   • Statisticians   • Data related Professionals   • Business Intelligence Professionals

Prerequisites for Learning Data Science Course

• High school mathematical skills      • Knowledge on MS Excel and MS office    • Lateral thinking     • Basic knowledge in Statistics

 Probability and Statistics for Data Science

“Facts are stubborn things, but statistics are pliable.”This module is aimed at preparing you for the very essential skill “thinking like a statistician”. You will learn:

  • Understanding the properties of attribute
  • Central tendencies (Mean, Median, Mode)
  • Measure of Spread (Range Variance Standard Deviation)
  • Basics of Probability
  • Expectation and Variance of a variable
  • Z- test

Probability theory

  • Random Variables
  • Probability theory
  • Conditional Probability
  • Bayes theorem

Deeper into probability distribution

  • Discrete Probability Distribution: Bernoulli, Binomial, Geometric, poison and properties of each.
  • Continuous Probability Distribution: Exponential, Normal distribution, t-distribution
  • Judgments and Conclusion from samples.
  • Inferential Statistics: Population from a sample and vice versa; Central Limit
  • Theorem, Sampling Distribution, Confidence Interval, Hypothesis Testing.
  • More Statistical testing: chi-square test, t-test, F-test and ANOVA
  1. Essential Engineering Skills in Big Data Analytics using Python

This module will introduce to reading, statistical analysis, visualization of data and then move to

designing, evaluating and implementing predictive models using most widely used tool Python.

  • Python basics: string, understanding data structures, functions, data manipulation, etc.
  • Python Libraries: Numpy, Pandas, Matplotlib.
  • Data Pre-processing: Binning, Filling, missing values, Standardization and Normalization, type conversion, train-test data split.
  • Hands-on implementation of all the pre-processing techniques.
  • Business Case Analysis:
  • Solving a Data Science problem
  • For business case you will be required to apply all the data pre-processing steps, and prepare the input for ML algorithms.
  • You need to design the solution and analyze for given business case
  1. Statistics and Probability in Decision Modeling

You will learn very powerful supervised learning methods, Linear Regression, Logistic Regression and Naïve Bayes Classifier to solve problems in prediction and classification.

  • Linear Regression
    • Relationship between variables: Regression (Linear, Multivariate Linear Regression) in prediction.
    • Understanding the summary output of Linear Regression
    • Residual Analysis
    • Identifying significant features, feature reduction using AIC, multi-collinearity check, observing influential points, etc.
    • Hypothesis testing of Regression Model
    • Confidence intervals of Slope
    • R-square and goodness of fit Influential Observation – Leverage
    • Multiple Linear Regression
    • Polynomial Regression
    • Categorical Variable in Regression
    • Logit function and interpretation
      • Hands-on Python Session on Logistic Regression using business case.
      • ROC
  • Naïve Bayes classifier
    • Review probability distributions, Joint and conditional probabilities
    • Model Assumptions, Probability estimation
    • Required data processing
    • Feature Selection
    • Classifier
  • Feature Reduction / Dimensionality reduction
    • Background: Eigen values, Eigen vectors, Orthogonality
    • Principal components analysis
  • Regularization methods
    • Lasso, Ridge and Elasticents

Time Series Analysis: An approach to analyze financial data and other forms of data based on their time dependent past values.

  • Trend analysis
  • Cyclical and Seasonal analysis
  • Smoothing; Moving averages; Auto-correlation; ARIMA
  • Application of Time Series in financial markets
  1. Methods and Algorithms in Machine Learning

Creating programs that use data to optimize without intervention

This module discuss the principle and ideas underlying the current practices of data mining and introduces to a powerful set of useful data analytics tools. At the end of the course, you will able to answer questions like “Which machine learning techniques is likely to work under which situations?”, “How to build a powerful recommendation engine?” etc.

From techniques perspective you will learn:

  • Rule based approach, distance based approach, mathematical modelling, etc.

Rule based Approach

  • Classification Rules
  • Indirect: from decision tree
  • Direct: Sequential covering
  • Association rules
  • How to combine clustering and classification;
  • A mathematical model for association analysis
  • Apriori: Constructs large itemsets with mini sup by iterations
  • More in rule based classifier
  • Manually derive the rules
  • Top down induction of decision tree
  • Attribute selection based on information theory approach

Distance Based Approach

  • Computational geometry; Voronoi Diagrams; Delaunay Triangulations
  • K- Nearest Neighbor algorithm; Wilson editing and triangulations
  • Hands on example of K – Nearest Neighbor using Python
  • Collaborative filtering and its applications areas

Mathematical Approach

  • Linear learning machines and Kernel space, Making kernels and working in feature space
  • Hands on example of SVM classification and regression problems using a businesscase in Python.

Ensemble Models

  • Bagging & boosting and its impact on bias and variance
  • Random forest
  • Gradient Boosting Machines and XGBoost

Unsupervised learning algorithm – Clustering

  • Different clustering methods, review of several distance measures
  • Iterative distance-based clustering
  • Dealing with continuous, categorical values in K-Means
  • Constructing a hierarchical clusters, K-Medoids, k-Mode and density based

clustering.

  • Test for stability check of clusters
  • Hands-on implementation of each of these methods in Python
  1. Foundations of Text Mining (Natural Language Processing)

This module aims the principles and ideas underlying text mining and social networks analytics.

  • Introduction to the Fundaments of information retrieval
  • TF and IDF
  • Thinking about the math behind text; Properties of words; Vector Space Model
  • Matrix factorization: SVD
  • Text Indexing
  • Inverted Indexes
  • Boolean query processing
  • Handling phrase queries, proximity queries
  • LSA
  • Relevance Ranking
  • Need for Relevance Ranking
  • Evaluation Metrics for Ranking
  • Link Analysis Algorithms
  • Page Rank
  • Text classification
  • Sentiment analysis

Artificial Intelligence

Artificial Intelligence changes the way people value data. Instead of merely viewing data, we work with organizations to create programs that plug into steady streams of data to learn and optimize in ways that no humans could replicate.

Training your computers to think like your employees

Deep Learning is the fastest growing field in Machine Learning, an approach to AI that has been revolutionizing several industries and playing a major role in changing the way we live. You will learn one of the most commonly used and important types of Neural Networks.

Implementing a connected neural network to magnify results of machine learning applications

Optical Character Recognition

Making leaps in the advancement of data entry for accuracy, speed, and efficiency

Artificial Neural Networks

  • Perceptron model and its limitations
  • Multi-layer perceptron and non-linear data
  • Learning using Back-propagation
  • ANNs for Classification and regression of structured data
  • Regularization – Dropout and Batch normalization

Convolutional Neural Networks

  • Deep architectures
  • Image Classification

Recurrent Neural Networks

  • Long short-term memory cells
  • Text classification
  • Sentiment analysis
  • Time-series

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