We are proud to launch Mentorship program in Data Science aiming at learning Analytics and Machine Learning. This is going to be 120 hours 3 months of extensive coaching with an option to do internship.
About the course:
Our data analyst certification helps you learn analytics tools and techniques, how to work with SQL databases, R and Python, how to create data visualizations, and apply statistics and predictive analytics in a business environment
- 120 hours of hand-on training
- Lot of industry based projects from every topic
- Dedicated mentoring session with industry experts
Skills you will learn:
Python Programming, R Programming, SQL Programming, Tableau, Statistics for Machine Learning, understanding of data structure and data manipulation, machine learning model building, deep learning
Duration:
Total duration of live classes: 120 hours
Fees: Contact us at +91 8008101590 (Call + Whatsapp)
Course Structure:
- INTRODUCTION
Introduction to the courseIntroduction to Data Science, Introduction to AnalyticsDesign, Thinking and Problem Statement, Mini Project 1Project 1 - Master Python Programming
Python Basics, Python Variables: int, float, string, bool, complex, Conditional statements, Loops, Python Collections: List, Tuple, Dictionary, Set, Frozenset, Mini Project 3, Functions and Methods, Class & Objects, Mini Project 4, Numpy, Working with CSV and Text files, Working with Database, Error Handling, Regular Expression, Project 2: Using Python concepts - Descriptive Statistics
Data and Types, Central Tendency: Mean, Median, Mode, Deviation: Range, Variance, Standard Deviation, BoxPlot and its importance, Mini Project 5, Frequency distribution and its importance, Mini Project 6, Scatter Plots and its importance, Mini Project 7
4 Data Visualization
Story Telling, Scipy, Pandas, Mini Project 8, Matplotlib: basic plots and advanced plots, Mini Project 9, Seaborn: basic plots and advanced plots, Mini Project 10, NLP: N-gram models of language, Project 3: NLP Web Scrapping, Project 4: Web scraping and visualization
5 Inferential Statistics
Probability: Discrete Probability Distribution: Binomial Distribution, PoissonContinuous Probability Distribution: Normal, t distribution, Exponential CorrelationMini Project 11
6 SQL Programming
Introduction to Database, Introduction to SQL, SQL JOIN and OPERATORS, CRUD operations on Tables, Data Wrangling with SQLProject 5
7 R programming
R Basics Data types, Loops, Data Visualization, Regression: Simple and Multiple Classification: KNN, Logistics, Clustering: K Means, Hierarchical Project 6
8 Data Science Methodology
Introduction Types of learning, Data Acquisition Data Wrangling, Model Development, Model Evaluation, Scikit-Learn package
9 Machine Learning
Regression: Simple linear, multiple linear, ridge, lasso, decision tree, random forest, Project 7 , Classification: svm, decision tree, random forest, naïve bayes, bagging, boosting, Project 8, Clustering: K means, Hierarchical Project 9, Association: Market Basket Analysis, Mini Project 12
10 Deep Learning
Neural Network – ANN, CNN, RNN, Autoencoders, Long Short-term memory (LSTM), Restricted Boltzman Machine (RBM), Project 10
11 Working with Tableau
Introduction to Visualization, Concepts: Filter, Join, Hierarchy, Groups, SetCharts and Dashboard, Forecasting and Clustering in Tableau, Business Stories
| 1 | INTRODUCTION |
| Introduction to the courseIntroduction to Data ScienceIntroduction to AnalyticsDesign Thinking and Problem StatementMini Project 1Project 1 | |
| 2 | Master Python Programming |
| Python BasicsPython Variables: int, float, string, bool, complexConditional statementsLoopsPython Collections: List, Tuple, Dictionary, Set, FrozensetMini Project 3Functions and MethodsClass & ObjectsMini Project 4NumpyWorking with CSV and Text filesWorking with DatabaseError HandlingRegular ExpressionProject 2: Using Python concepts | |
| 3 | Descriptive Statistics |
| Data and TypesCentral Tendency: Mean, Median, ModeDeviation: Range, Variance, Standard DeviationBoxPlot and its importanceMini Project 5Frequency distribution and its importanceMini Project 6Scatter Plots and its importance Mini Project 7 | |
| 4 | Data Visualization |
| Story TellingScipyPandasMini Project 8Matplotlib: basic plots and advanced plotsMini Project 9Seaborn: basic plots and advanced plotsMini Project 10NLP: N-gram models of languageProject 3: NLPWeb ScrappingProject 4: Web scraping and visualization | |
| 5 | Inferential Statistics |
| ProbabilityDiscrete Probability Distribution: Binomial Distribution, PoissonContinuous Probability Distribution: Normal, t distribution, Exponential CorrelationMini Project 11 | |
| 6 | SQL Programming |
| Introduction to DatabaseIntroduction to SQLSQL JOIN and OPERATORSCRUD operations on TablesData Wrangling with SQLProject 5 | |
| 7 | R programming |
| R BasicsData typesLoopsData VisualizationRegression: Simple and MultipleClassification: KNN, LogisticsClustering: K Means, Hierarchical Project 6 | |
| 8 | Data Science Methodology |
| IntroductionTypes of learningData AcquisitionData WranglingModel DevelopmentModel EvaluationScikit-Learn package | |
| 9 | Machine Learning |
| Regression: Simple linear, multiple linear, ridge, lasso, decision tree, random forestProject 7Classification: svm, decision tree, random forest, naïve bayes, bagging, boostingProject 8Clustering: K means, HierarchicalProject 9Association: Market Basket AnalysisMini Project 12 | |
| 10 | Deep Learning |
| Neural Network – ANN, CNN, RNNAutoencodersLong Short-term memory (LSTM)Restricted Boltzman Machine (RBM)Project 10 | |
| 11 | Working with Tableau |
| Introduction to VisualizationConcepts: Filter, Join, Hierarchy, Groups, SetCharts and DashboardForecasting and Clustering in TableauBusiness Stories |
