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
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
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
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
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