Data Science Training Course in Hyderabad, India
Data Science Training Course in Hyderabad, India
SoftBloom Data Science training course held in Hyderabad helps you master data analytics, business analytics, data modeling, machine learning algorithms, KMeans Clustering, NaïveBayes, etc. You will be learning R statistical computing, building a recommendation engine for commerce, recommending movies and deploy market basket analysis in the retail sector.
About Data Science Training Course
SoftBloom Data Science Institute provides the most advanced Data Science & Analytics training course in Hyderabad that includes the various components like machine learning, cluster analysis, data mining, cleansing, transformation, deploying data visualization among other things.
What you will learn in this Data Scientist Certification Training?
 Data analysis, project lifecycle and data science in the real world
 Work with machine learning algorithms
 Master the techniques of evaluation, experimentation and project deployment
 Analysis segmentation using clustering and the technique of prediction
 Learning how to integrate R with Hadoop
 The various steps in the installation of Impala
 Understanding the various roles and responsibilities of Data Scientist
 Data science projects, analytics, and recommender systems.
Who should take this Data Science Online Training Course?
 Big Data Experts, BI & Analyst professionals
 Big Data Statisticians
 Machine Learning professionals
 Predictive Analytics & Information Architects
 Those looking for Data Science career
What are the prerequisites for learning Data Science?
What are the Data Science job opportunities in Hyderabad?
What is the Data Science market trend in Hyderabad?
Why should you take the Online Data Scientist Course in Hyderabad?
 Data Scientist is the best job of the 21st century – Harvard Business Review
 Global Big Data market to reach $122B in revenue by 2025 – Frost & Sullivan
 The US alone could face a shortage of 1.4 1.9 million Big Data Analysts by 2018 – Mckinsey
The data scientist is the best job of the 21st century as quoted by the Harvard Business Review. So due to this, there is a premium attached to the role of a Data Scientist. The role of a Data Scientist is central to the working of today’s datadriven organizations. There is an urgent need to convert data into insights and be the bridge between the technical and nontechnical departments and take highlevel business decisions which is where the role of Data Scientist gains increased importance. SoftBloom Data Science training in Hyderabad can help you get the Data Scientist Certification and grab the best j
1.Introduction to Data Science
 What is data science?
 How is data science different from Bi and Reporting?
 Who are data scientists?
 What skillsets are required?
 What do they do?
 What kind of projects they work on?
2.Business statistics
 Data types
 Continuous variables
 Ordinal Variables
 Categorical variables
 Time Series
 Miscellaneous
 Descriptive statistics
 Sampling
 Need for Sampling?
 Different types of Sampling
 Simple random sampling
 Systematic sampling
 Stratified Sampling
 Data distributions
 Normal Distribution – Characteristics of a normal distribution
 Binomial Distribution
 Inferential statistics
 Hypothesis testing
 Type I error
 Type II error
 Null and alternate hypothesis
 Reject or acceptance criterion
3.Introduction to R
 A Primer to R programming
 What is R? similarities to OOP and SQL
 Types of objects in R – lists, matrices, arrays, data.frames
 Creating new variables or updating existing variables
 IF statements and conditional loops – For, while
 String manipulations
 Sub setting data from matrices and frames
 Casting and melting data to long and wide
 Merging datasets
4.Exploratory data analysis and visualization
 Getting data into R – reading from files
 Cleaning and preparing the data – converting data types (Character to numeric )
 Handling missing values – Imputation or replacing with place holder values
 Visualization in R using ggplot2(plots and charts) – Histograms, bar charts, box plot, scatterplots
 Adding more dimensions to the plots
 Visualization using Tableau( Introduction)
 Correlation – Positive , negative and no correlation
 What is a spurious correlation
 Correlation vs. causation
5.Introduction to Python:
 Understanding the reason of Python’s popularity
 Basics of Python: Operations, loops, functions, dictionaries
 Advanced operations with text: Finding, Sequencing and basic analytics
 Groundup for DeepLearning
6.Predictive analytics
 Different types of predictive analytics – prediction, forecasting, optimization, segmentation
 Supervised learning
 Prediction (Linear)
 Simple Linear Regression
 Assumptions
 Model development and interpretation
 Sum of least squares
 Model validation – tests to validate assumptions
 Multiple linear regression
 Disadvantages of linear models
Classification
 Logistic Regression
 Need for logistic regression
 Logit link function
 Maximum likelihood estimation
 Model development and interpretation
 Confusion Matrix – error measurement
 ROC curve
 Measuring sensitivity and specificity
 Advantages and disadvantages of logistic regression models
 Decision trees 1. 0
 Classification and Regression trees(CART)
 Process of tree building
 Entropy and Gini Index
 Problem of over fitting
 Pruning a tree back
 Trees for Prediction (Linear) – example
 Tress for classification models – example
 Advantages of tree based models?
 KNN – K nearest neighbors
 Advantages and disadvantages of KNN
 ReSampling and Ensembles Methods
 Bagging
 Random Forests
 Boosting – Gradient boosting machines
 Advanced methods
 Support Vector machines
 Neural networks
 Introduction to deep learning
 Introduction to online learning
 UnSupervised learning
Cluster analysis
 Hierarchical clustering
 KMeans clustering
 Distance measures
 Applications of cluster analysis – Customer Segmentation
 Time series analysis – Forecasting
 Simple moving averages
 Exponential smoothing
 Time series decomposition
 ARIMA Collaborative filtering
 User based Filtering
 Item based Filtering
7.Model validation and deployment
 Error measurement
 RMSE – Root Mean squared error
 Misclassification rate
 Area under the curve (AUC)
8.Practical use cases and best practices
 Business problem to an analytical problem
 Problem definition and analytical method selection
 Guidelines in model development
9.Introduction to bigdata and other tools ( Python and RServer)
 Big data and analytics?
 Leverage Big data platforms for Data Science
 Introduction to evolving tools g Spark
 Machine learning with Spark
 Introduction to Azure cloud and BigData computing over cloud
 Creation of RServer clusters
 Computation of BigData ML algorithms over the Azure cloud
11.Introduction to Deep Learning
 What is DL and how does it score better over traditional MLs?
 Convolutional and Perceptron models
 Comparison between DL and ML performances over the MNIST dataset
12.Analytical Visualisation with Tableau
 Why is it important for DataAnalyst
 Tableau workbook walkthrough
 Instruction of creation of your own workbooks
 Demo of few more workbooks
13.Offerings from Kelly.
 Mock interviews questions and case studies walkthrough over Azure Cortana gallery
 Guidance to prepare resumes
 Information on companies and industry trends on data science
obs.
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