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Mastering in Data Science 3RITPL-1 (1).pdf
- 1. Mastering in Data Science POWERED BY 3RI TECHNOLOGIES Premium Corporate Training 3ri technol ogi es. com
- 2. 3RI is an Advanced Technology Education Platform providing advanced and professional training in Data Analytics that helps freshers and professionals rise to the advanced skills requirements of the current industry. Powered by 3RI Technologies, these classroom training sessions are industry-oriented courses on Data Science, Machine Learning, and Python by top-class faculty who have more than 7+ years of experience in the same domain. The curriculum is designed by industry experts, who have real-time scenario-based exercises and case studies and projects using databases from companies like Walmart, Amazon, Google, etc. This training is supported with teaching assistance which allows individuals to develop hands-on skills by applying the concepts and becoming technically proficient. About us Company Highlights 3ri technol ogi es. com
- 3. Faculty Subject & Tracks Curriculum Placement Support FAQ's Who We Are? Coordinates 1. 2. 3. 4. 5. 6. 7. Index Brochure Highlights 3ri technol ogi es. com
- 4. We have the best team of trainers who are working at senior-most positions in top IT companies like Infosys, Cognizant, Accenture, and so on. Faculty The Representers 3ri technol ogi es. com
- 5. 3RI has a holistically designed curriculum designed by our expert team of trainers with 8+ years of industry experience. 3RI offers the perfect recipe for success through regular assignments and projects. Subjects & Tracks What makes 3RI different? 3ri technol ogi es. com
- 6. 1. Fundamentals of Data Science and Mathematical Statistics Fundamentals of Statistics & Data Science 2. Mathematics For Data Science Introduction to Data Science Need of Data Science BigData and Data Science Data Science and machine learning Data Science Life Cycle Data Science Platform Data Science Use Cases Skill Required for Data Science Optimization Theory Of optimization Gradients Descent 1. 2. 3ri technol ogi es. com Linear Algebra Vectors Matrices 1. 2. 3. Introduction to Statistics Descriptive vs Inferential Statistics Types of data Measures of central tendency and dispersion Hypothesis & inferences Hypothesis Testing Confidence Interval Central Limit Theorem
- 7. Fundamentals of Statistics & Data Science 4. Probability and Probability Distributions Probability Theory Conditional Probability Data Distribution Distribution Functions Normal Distribution Binomial Distribution 1. 2. 3ri technol ogi es. com
- 8. MS EXCEL: Data handling on SPREADSHEET What is Excel? Why Use Excel? Excel Overview Excel Ranges, Selection of Ranges Excel Fill, Fill Copies, Fill Sequences, Sequence of Dates Excel adds, move, and delete cells Excel Formulas Relative and Absolute References 1. Using a Spreadsheet 3ri technol ogi es. com SUM AVERAGE COUNT MAX & MIN RANDBETWEEN TRIM LEN CONCATENATE TODAY & NOW 2. Functions
- 9. MS EXCEL: Data handling on SPREADSHEET Excel IF Function Excel If Function with Calculations How to use COUNT, COUNTIF, and COUNTIFS Function? Excel Advanced If Functions Excel Data Analysis - Data Visualization Visualizing Data with Charts Chart Elements and Chart Styles Data Labels Quick Layout 3. Advanced Functions 4. Data Visualization 3ri technol ogi es. com
- 10. RDBMS: SQL in detail An Introduction to RDBMS & SQL Data Retrieval with SQL Pattern matching with wildcards Basics of sorting Order by clause Aggregate functions Group by clause Having clause Nested queries Inner join Multi join Outer join 3ri technol ogi es. com Adding and Deleting columns Changing column name and Data Type Creating Table from existing Table Changing Constraints Foreign key
- 11. 1. An Introduction to Python Python for Data Science 2. Conditional Statement, Loops and File Handling Why Python, its Unique Feature, and where to use it? Python Environment Setup/shell Installing Anaconda Understanding the Jupyter notebook Python Identifiers, Keywords Discussion about installed modules and packages Python Data Types and Variable Condition and Loops in Python Decorators Python Modules & Packages Python Files and Directories manipulations Use various files and directory functions for OS operations 3ri technol ogi es. com
- 12. 3. Python Core Objects & Functions Python for Data Science 4. Introduction to NumPy Built-in modules (Library Functions) Numeric and Math’s Module String/List/Dictionaries/Tuple Complex Data structures in Python Python built-in function Python user defined functions Array Manipulation Array I/O Importing Files with Numpy 3ri technol ogi es. com Array Operations Arrays Functions Array Mathematics 5. Data Manipulation with Pandas Data Frames I/O Selection in DFs Retrieving in DFs Applying Functions Reshaping the DFs - Pivot Combining DFs Merge, Join Data Alignment
- 13. Python for Data Science 6. SciPy Matrices Operations Create matrices Inverse, Transpose, Trace, Norms, Rank, etc Matrices Decomposition Eigen Values & vectors SVDs 3ri technol ogi es. com 7. Visualization with Seaborn 8. Visualization with Matplotlib Visualizing the Distribution of a Dataset Selection color palettes Seaborn Installation Introduction to Seaborn Basics of Plotting Plots Generation Matplotlib Installation Matplotlib Basic Plots & it's Containers Matplotlib components and properties Pylab & Pyplot Scatter plots 2D Plots
- 14. Python for Data Science 9. SciKit Learn Basics Data Loading Train/Test Data generation Preprocessing Generate Model Evaluate Models 10. Descriptive Statistics Observations, variables, and data matrices Types of variables Measures of Central Tendency Arithmetic Mean / Average Merits & Demerits of Arithmetic Mean and Mode Merits & Demerits of Mode and Median Merits & Demerits of Median Variance 1. 2. 3. 3ri technol ogi es. com Histograms Bar Graphs Pie Charts Box Plots Customization Store Plots
- 15. 11. Probability Basics Python for Data Science 12. Probability Distributions Notation and Terminology Unions and Intersections Conditional Probability and Independence Random Variable Probability Distributions Probability Mass Function Parameters vs. Statistics Binomial Distribution Poisson Distribution Normal Distribution Standard Normal Distribution Central Limit Theorem Cumulative Distribution function 13. Tests of Hypothesis Large Sample Test Small Sample Test One Sample: Testing Population Mean Hypothesis in One Sample z-test Two Sample: Testing Population Mean 3ri technol ogi es. com
- 16. Python for Data Science 14. Data Analysis One Sample t-test - Two Sample t- test Paired t-test Hypothesis in Paired Samples t-test Chi-Square test Case study- Netflix Deep analysis on Netflix data 3ri technol ogi es. com
- 17. Machine learning 1. Exploratory Data Analysis 2. Feature Selection Data Exploration Missing Value handling Outliers Handling Feature Engineering Importance of Feature Selection in Machine Learning Filter Methods Wrapper Methods Embedded Methods 3. Machine Learning: Supervised Algorithms Classification Introduction to Machine Learning Logistic Regression Naïve Bays Algorithm K-Nearest Neighbor Algorithm Decision Trees SingleTree Random Forest Support Vector Machines Model Ensemble 1. 2. 3ri technol ogi es. com
- 18. Machine learning 4. Machine Learning: Regression Model Evaluation and performance K-Fold Cross-Validation ROC, AUC, etc... Hyper parameter tuning 1. 2. 1.Regression 2.classification Simple Linear Regression Multiple Linear Regression Decision Tree and Random Forest Regression 5. Machine Learning: Unsupervised Learning Algorithms Similarity Measures Cluster Analysis and Similarity Measures 3ri technol ogi es. com 6. Ensemble Algorithms Bagging Boosting Voting Stacking K-means Clustering
- 19. Machine learning Hierarchical Clustering Principal Components Analysis Association Rules Mining & Market Basket Analysis 3ri technol ogi es. com 7. Recommendation Systems Collaborative filtering model Content-based filtering model. Hybrid collaborative system
- 20. Artificial Intelligence & Deep Learning 1.Artificial Intelligence An Introduction to Artificial Intelligence History of Artificial Intelligence Future and Market Trends in AI Intelligent Agents – Perceive-Reason- Act Loop Search and Symbolic Search Constraint-based Reasoning Simple Adversarial Search (Game- Playing) Neural Networks and Perceptions Understanding Feedforward Networks Boltzmann Machines and Autoencoders Exploring Backpropagation 3ri technol ogi es. com 3.Natural Language Processing Understanding Sensor Processing Natural Language Processing Studying Neural Elements Convolutional Networks Recurrent Networks Long Short-Term Memory (LSTM) Networks Natural Language Processing NLP in Python 2.Deep Networks & Structured Knowledge
- 21. Artificial Intelligence & Deep Learning Studying Deep Learning Artificial Neural Networks ANN Intuition Plan of Attack Studying the Neuron The Activation Function Working of Neural Networks Exploring Gradient Descent Stochastic Gradient Descent Exploring Back propagation 3ri technol ogi es. com 4.Artificial and Conventional Neural Network Understanding Artificial Neural Network Building an ANN Building Problem Description Evaluation the ANN Improving the ANN Tuning the ANN
- 22. Artificial Intelligence & Deep Learning 3ri technol ogi es. com 5.Image Processing / Machine Vision Image basics Loading and saving images Thresholding Bluring Masking Image Augmentation CNN Intuition Convolution Operation ReLU Layer Pooling and Flattening 6.Conventional Neural Networks Full Connection Softmax and Cross-Entropy Building a CNN Evaluating the CNN Improving the CNN Tuning the CNN 7.Recurrent Neural Network Recurrent Neural Network RNN Intuition The Vanishing Gradient Problem LSTMs and LSTM Variations
- 23. Artificial Intelligence & Deep Learning Practical Intuition Building an RNN Evaluating the RNN Improving the RNN Tuning the RNN 3ri technol ogi es. com Introduction to Time series data Data cleaning in time series Pre-Processing Time-series Data Predictions in Time Series using ARIMA, Facebook Prophet models. 8.Time Series Data GIT: Complete Overview Introduction to Git & Distributed Version Control Life Cycle Create clone & commit Operations Push & Update Operations Stash, Move, Rename & Delete Operations
- 24. Machine Learning in Cloud Machine Learning Features & Services Using python in Cloud How to access Machine Learning Services Lab on accessing Machine learning services Uploading Data Preparation of Data Applying Machine Learning Model Deployment by Publishing Models using AWS or other cloud computing 3ri technol ogi es. com
- 25. Data Visualization with Tableau 1.Introduction to Data Visualization & Power of Tableau Architecture of Tableau Product Components Working with Metadata and Data Blending Data Connectors Data Model File Types Dimensions & Measures Data Source Filters Creation of Sets 2.Scatter Plot Gantt Chart Funnel Chart Waterfall Chart Working with Filters Organizing Data and Visual Analytics Working with Mapping Working with Calculations and Expressions Working with Parameters Charts and Graphs Dashboards and Stories 3ri technol ogi es. com
- 26. Machine Learning end to end Project blueprint Case study on real data after each model. Regression predictive modeling - E-commerce Classification predictive modeling - Binary Classification Case study on Binary Classification – Bank Marketing Case study on Sales Forecasting and market analysis Widespread coverage for each Topic Various Approaches to Solve Data Science Problem Pros and Cons of Various Algorithms and approaches Amazon-Recommender Image Classification Sentiment Analysis Project Work and Case Studies 3ri technol ogi es. com
- 27. How to collect data?, how to justify the right features? , Which ML / DL model is best in this situation? How much data is enough? How to have CI/CD in the project? How to do Deployment of Project to cloud? The insurance company wants to decide on the premium using various parameters of the client. It’s an important problem to keep the clients and attract new ones. Project Domain: FINANCE 3ri technol ogi es. com By completing this Project you will learn:
- 28. How to handle image data? How to preprocess and augment image data? How to choose the right model for the image process? How to apply transfer learning in image processing? How to do incremental learning & CI/CD in the project? How to do Deployment of Project to cloud? A hospital wants to automate the Detection of pneumonia in X-rays using image processing. Image Processing in Health care 3ri technol ogi es. com By completing this Project you will learn:
- 29. How do convert text to the right representation? How to preprocess text data? How to select the right ML/DL model for text data? How to do transfer learning in Text Analytics? How to do CI/CD in a text analytics project? How to do Deployment of Project to cloud? One of the companies wants to automate applicant’s level in English communication. Create a ML/DL model for this task. Natural Language Processing 3ri technol ogi es. com By completing this Project you will learn:
- 30. How to handle time-series data? How to preprocess time series data? How to create ML/DL model for Time-series Data? How to do CI/CD in a text analytics project? How to do Deployment of Project to cloud? A mechanical company wants to perform predictive maintenance of engine parts. This enables the company to efficiently change parts before the machine fails. Mechanical 3ri technol ogi es. com By completing this Project you will learn:
- 31. How to handle time-series data? How to preprocess time series data? How to create ML/DL model for Time-series Data? How to do CI/CD in a text analytics project? How to do Deployment of Project to cloud? Predict the sales/demand of a product of a company. Sales / Demand forecasting of the product will help the company efficiently manage the resources. Create a ML/DL model for this problem. Sales / Demand Forecasting 3ri technol ogi es. com By completing this Project you will learn:
- 32. Placement at Our Client's Location Interview Preparartion Resume Workshop by Experts Real World Application Integration in Resume Exclusive Sessions from Experts Placement Support 3ri technol ogi es. com
- 33. Do I need to purchase any Software ? No, we provide necessary installation guides of the software required. What are the specific System Requirements ? 8 GB RAM, 500GB Hard disk (i3 Processor). What are Projects ? Projects are real world datasets from companies like Nike, Yelp,Amazon, Netflix etc. that are provided to our students. FAQ's 3ri technol ogi es. com
- 34. Siddhant Sir DATA SCIENTIST Ranjeet Sir CLOUD ARCHITECT Krishna Ma'am PYTHON EXPERT Who We Are? 3ri technol ogi es. com * Images used for representation purpose
- 35. +91 830 810 3366 / +91 96238 68215 +91 866 965 8215 / +91 74100 73340 MAILING ADDRESS 3RI Technologies Pimple Saudagar 405, Rainbow Plaza, Pimple Saudagar, Rahatani, Pune - 411017 [email protected] EMAIL ADDRESS PHONE NUMBER Coordinates 3ri technol ogi es. com