There is huge scope for every IT enthusiast to look into this newly growing field. More than programming, this field is all filled with the purpose of saving and troubleshooting the data. Blend is the one such a kind of Data Science training Institute in Pune

- Data Science motivating examples -- Money ball, okcupid, Nate Silver, Netfilx, LinkedIn,
- Introduction to Analytics, Types of Analytics,
- Introduction to Analytics Methodology
- Analytics Terminology, Analytics Tools
- Introduction to Big Data
- Introduction to Machine Learning

- What is Data Science?
- Common Terms in Analytics
- Types of problems and business objectives in various industries
- How leading companies are harnessing the power of analytics?
- Overview of analytics tools & their popularity
- List of steps in Analytics projects
- Identify the most appropriate solution design for the given problem statement
- Why R for data science?

- Introduction to R
- Install R & R studio
- Perform basic operations in R using command line
- Learn the use of IDE R Studio
- Use the ‘R help’ feature in R

- Variables in R
- Scalars
- Vectors
- Matrices
- List
- Data frames
- Using c, Cbind, Rbind, attach and detach functions in R
- Factors

- Data sorting
- Cleaning data
- Recoding data
- Merging data
- Slicing of Data
- Apply functions

- Topics
- Numerical functions
- Character functions
- Operators in R
- Arithmetic operator
- Relational operator
- Logical operator
- Assignment operator

- If loop
- For loop
- While loop
- Break, next and pass statement

- Reading Data from excel, csv and txt files
- Writing Data
- Basic SQL queries in R
- Connecting to the database
- Dealing with Date values

- Box plot
- Histogram
- Pareto charts
- Pie graph
- Line chart
- Scatterplot
- Developing Graphs

- Concept of model in analytics and how it is used?
- Common terminology used in analytics & modeling process
- Popular modeling algorithms
- Different Phases of Predictive Modeling

- Need for structured exploratory data
- EDA framework for exploring the data and identifying any problems with the data (Data Audit Report)
- Identify missing data
- Identify outliers data
- Visualize the data trends and patterns

- Introduction - Applications
- Assumptions of Linear Regression
- Building Linear Regression Model
- Understanding standard metrics (Variable significance, R-square/Adjusted R-square, Global hypothesis ,etc)
- Assess the overall effectiveness of the model
- Interpretation of Results

- Introduction - Applications
- Building Logistic Regression Model
- Understanding standard model metrics (Concordance, Variable significance, Hosmer Lemeshov Test, Gini, KS, Misclassification, ROC Curve etc)
- Validation of Logistic Regression Models
- Standard Business Outputs (ROC Curve, Probability Cut-offs, Lift charts, Model equation, Drivers or variable importance, etc)

- Decision Trees - Introduction - Applications
- Types of Decision Tree Algorithms
- Construction of Decision Trees through Simplified Examples; Choosing the "Best" attribute at each Non-Leaf node; Entropy; Information Gain, Gini Index, Chi Square, Regression Trees
- Generalizing Decision Trees; Information Content and Gain Ratio; Dealing with Numerical Variables; other Measures of Randomness
- Pruning a Decision Tree; Cost as a consideration
- Decision Trees - Validation
- Overfitting - Best Practices to avoid

- Concept of Ensembling
- Random forest (Logic, Practical Applications)

- Motivation for Support Vector Machine & Applications
- Interpretation of Outputs and Fine tune the models with hyper parameters

- What is KNN & Applications?
- KNN for missing treatment
- KNN For solving regression problems
- KNN for solving classification problems
- Validating KNN model
- Model fine tuning with hyper parameters

- Concept of Conditional Probability
- Bayes Theorem and Its Applications
- Naïve Bayes for classification
- Applications of Naïve Bayes in Classifications

- Introduction - Applications
- Basic Techniques - Averages, Smoothening, etc
- Advanced Techniques - AR Models, ARIMA, etc
- Understanding Forecasting Accuracy - MAPE, MAD, MSE, etc

- What is segmentation & Role of ML in Segmentation?
- K-Means Clustering
- Expectation Maximization
- Principle component Analysis (PCA)

- What is Data Science?
- Common Terms in Analytics
- Types of problems and business objectives in various industries
- Overview of analytics tools & their popularity
- List of steps in Analytics projects
- Identify the most appropriate solution design for the given problem statement
- Why Python for data science?

- Overview of Python- Starting with Python
- Introduction to installation of Python
- Introduction to Python IDE's
- Understand Jupyter notebook
- Concept of Packages/Libraries - Important packages(NumPy, SciPy, scikit-learn, Pandas, Matplotlib, etc)
- Installing & loading Packages & Name Spaces
- Data Types & Data objects/structures (strings, Tuples, Lists, Dictionaries)
- List and Dictionary Comprehensions
- Basic Operations
- Reading and writing data
- Simple plotting
- Control flow & conditional statements
- How to create class and modules and how to call them?

- Numpy, pandas, matplotlib, scikitlearn etc

- Importing Data from various sources (Csv, txt, excel etc)
- Viewing Data objects - subsetting, methods
- Exporting Data to various formats
- Important python modules: Pandas

- Cleansing Data with Python
- Data Manipulation steps(Sorting, filtering, duplicates, merging, appending, subsetting, derived variables, sampling, Data type conversions, renaming, formatting etc)
- Data manipulation tools(Operators, Functions, Packages, control structures, Loops, arrays etc)
- Normalizing data
- Formatting data
- Important Python modules for data manipulation (Pandas, Numpy, re, math, string, datetime etc)

- Introduction exploratory data analysis
- Descriptive statistics, Frequency Tables and summarization
- Univariate Analysis (Distribution of data & Graphical Analysis)
- Bivariate Analysis(Cross Tabs, Distributions & Relationships, Graphical Analysis)
- Creating Graphs- Bar/pie/line chart/histogram/ boxplot/ scatter/ density etc)
- Important Packages for Exploratory Analysis(NumPy Arrays, Matplotlib, Pandas etc)

- Basic Statistics - Measures of Central Tendencies and Variance
- Building blocks - Probability Distributions - Central Limit Theorem
- Inferential Statistics -Sampling - Concept of Hypothesis Testing
- Statistical Methods - Z/t-tests( One sample, independent, paired), Anova, Correlations and Chi-square
- Important modules for statistical methods: Numpy, Scipy, Pandas

- Concept of model in analytics and how it is used?
- Common terminology used in analytics & modeling process
- Popular modeling algorithms
- Different Phases of Predictive Modeling

- Need for structured exploratory data
- EDA framework for exploring the data and identifying any problems with the data (Data Audit Report)
- Identify missing data
- Identify outliers data
- Visualize the data trends and patterns

- Introduction - Applications
- Assumptions of Linear Regression
- Building Linear Regression Model
- Understanding standard metrics (Variable significance, R-square/Adjusted R-square, Global hypothesis ,etc)
- Assess the overall effectiveness of the model
- Interpretation of Results

- Introduction - Applications
- Building Logistic Regression Model
- Understanding standard model metrics (Concordance, Variable significance, Hosmer Lemeshov Test, Gini, KS, Misclassification, ROC Curve etc)
- Validation of Logistic Regression Models
- Standard Business Outputs (ROC Curve, Probability Cut-offs, Lift charts, Model equation, Drivers or variable importance, etc)

- Decision Trees - Introduction - Applications
- Types of Decision Tree Algorithms
- Construction of Decision Trees through Simplified Examples; Choosing the "Best" attribute at each Non-Leaf node; Entropy; Information Gain, Gini Index, Chi Square, Regression Trees
- Generalizing Decision Trees; Information Content and Gain Ratio; Dealing with Numerical Variables; other Measures of Randomness
- Pruning a Decision Tree; Cost as a consideration
- Decision Trees - Validation
- Overfitting - Best Practices to avoid

- Concept of Ensembling
- Random forest (Logic, Practical Applications)

- Motivation for Support Vector Machine & Applications
- Interpretation of Outputs and Fine tune the models with hyper parameters

- What is KNN & Applications?
- KNN for missing treatment
- KNN For solving regression problems
- KNN for solving classification problems
- Validating KNN model
- Model fine tuning with hyper parameters

- Concept of Conditional Probability
- Bayes Theorem and Its Applications
- Naïve Bayes for classification
- Applications of Naïve Bayes in Classifications

- Introduction - Applications
- Basic Techniques - Averages, Smoothening, etc
- Advanced Techniques - AR Models, ARIMA, etc
- Understanding Forecasting Accuracy - MAPE, MAD, MSE, etc

- What is segmentation & Role of ML in Segmentation?
- K-Means Clustering
- Expectation Maximization
- Principle component Analysis (PCA)

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- Hands on Project Experience exposures in the Lab session
- Real Time case studies to practice
- Free Technical Support after Course Completion
- Back up Classes Available
- LAB Facility
- Free Wifi to learn subject
- Latest Study Material
- Fast Track and Normal Batches available

- We send you for interviews till you get a job
- We get your Resume Ready to attend interviews
- Interview Preparation Support
- Write Technical Exams before attending Interviews
- Mock Interviews
- Pre-Requisite: Job Seekers, any Graduates, Software Developer, Fresher , web developers, web designers, SEO specialists
- Projects: You Work on Live Projects
- Latest and Update Course Contents as per corporate standards.
- Personal Attention to make Students Web Developer Experts

Suitable for: Fresher or Experienced who want to make career in Data Science.

We are the one Data Science training Institutes in Pune that offers good course support for the candidates throughout the course. With the increased demand for big data analytics with the future needs of the information technology, there is scope for every IT enthusiast to look into this newly growing field. More than programming, this field is all filled with the purpose of saving and troubleshooting the data. Blend is the one such a kind of- Managers
- Data analysts
- Business analysts
- Operators
- Job Seekers
- End users
- Developers
- Fresher/Graduates
- IT professionals

Amritesh Deshmukh 1 day ago

The training which I undergone was manual testing. I have learnt a lot and the training was good. I have learnt the basics too. Now I am able to do a project on my own with the help of knowledge I have gained from Blend Infotech.

Balkrishanan K 5 day ago

Complete Real time and best training in pune for Manual Testing.The trainer is vast experienced and MNC Expert. I am thankful to Blend Infotech simply great training center in Pune for Manual Testing.

Lomesh Borole 12 days ago

Friends if you want to learn Anything in Information Technology Blend is the best place which I can suggest. His training is really in Practical Manner, I got Placement after finishing my course there. Thanks for your Real time Training. Thanks to Blend Infotech also.

Khushal Rathod 14 days ago

I did manual Java in Blend InfoTech staff is very kind in teaching part and also supporting in intimating openings for job.class is good in easy understanding the concept in very clear.

Vijay Sawant 15 days ago

I have attended manual testing training. The faculty was very good and he explained all the concepts of manual testing with real time scenario. I also got support in developing my own Project. They taking care of the students for placements.Overall experience in Blend Infotech is very good.

1st Floor, Deccan Corner,

Opp. R-Deccan Mall/ KFC,

Near Deccan Bus stop

J.M Road, Deccan,

Shivaji Nagar, Pune- 04.

Office: 020-48618772,

Cell: 8087088772

7/1 Shreeyash Building,

Opp. Akurdi Railway Station,

Dharmaraj Chowk.

Nigdi (Akurdi) Pune-44

Mob: 8793008772

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