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- 1. PYTHON Core Python Fees:Rs7500 Data Analysis, Data Visualization, Machine Learning, Python with Data Science Fees: Rs7500 A1/17, Top Floor, Opposite Metro Pillar no: 636, Main Najafgarh ,Road, Janakpuri New Delhi – 110058 Phone no-011-4166-8088, 90155-96280, 9313565406, website-www.balujalabs.in
- 2. Core Python Part -1Core Python: Lesson 1 “Getting started with Python Programming Lesson 1 Getting started with Python programming • Overview • Introductory Remarks about Python • A Brief History of Python • How python is differ from other languages • Python Versions • Installing Python and Environment Setup • IDLE • Getting Help • How to execute Python program • Writing your first Python program Lesson 2 Variables, Keywords and Operators • Variables • Memory mapping of variables • Keywords in Python • Comments in python • Operators • Arithmetic Operators • Assignment Operators • Comparison Operators • Logical Operators • Membership Operators • Identity Operators • Bitwise Operators • Basics I/O and Type casting • Getting user input Lesson 3 Data types in Python • Numbers • Strings • Lists • Tuples • Dictionary • Sets Lesson 4 Numbers and Strings • Introduction to Python ‘Number’ & ‘string’data types • Properties of a string • String built-in functions • Programming with strings • String formatting Lesson 5 Lists and Tuples • Introduction to Python ‘list’data type • Properties of a list • List built-in functions • Programming with lists • List comprehension • Introduction to Python ‘tuple’ data type • Tuples as Read only lists
- 3. • Lesson 6Dictionary and Sets • Introduction to Python ‘dictionary’data type • Creating a dictionary • Dictionary built-in functions • Introduction to Python ‘set’ data type • Set and set properties • Set built-in functions Lesson 7 Decision making & Loops • Introduction of Decision Making • Control Flow and Syntax • The if Statement • The if...else Statement • The if...elif...else Statement Lesson 8 User defined Functions • Introduction of functions • Function definition and return • Function call and reuse • Function parameters • Function recipe and docstring • Scope of variables • Recursive functions • Lambda Functions / Anonymous Functions • Map , Filter & Reduce functions Lesson 9 Modules and Packages • Modules • Importing module • Standard Module - sys • Standard Module - OS • The dir Function • Packages Data Analysis • Indexing and slicing • Manipulating array shapes • Sorting arrays • Creating array views and copies • I/O with NumPy • Numpy Exercises MODULE 3 WORKING WITH PANDAS • Introduction to Pandas • Data structure of pandas • Pandas Series • Pandas dataframes • Data aggregation with Pandas • DataFrames Concatenating and appending Part -2 Data Analysis: MODULE 1 GETTING STARTED WITH PYTHON LIBRARIES • What is data analysis? • Why python for data analysis? • Essential Python Libraries Installation and setup • Ipython MODULE 2 NUMPY ARRAYS • Introduction to Numpy • Numpy Arrays • Numpy Data types • Numpy Array Indexing
- 4. Data Visualization • Part-3 Data Visualization: MODULE 1 • Line Plots • Bar Plots • Pie Plots • Scatter plots • Histogram Plots • Saving plots to file • Plotting functions in matplotlib • Matplotlib MODULE 2 Seaborn • I ntroduction of Seaborn • Distribution Plots • Categorical Plots • Matrix Plots • Bar Plots • Box Plots • Strip Plots • Violin Plots • Clustermap Plots • Heatmaps Plots • KDE Plots • Regression Plots • 12. Style and Color • 14. Seaborn Exercise MODULE 3 Plotly and Cufflinks • Introduction to Plotly and Cufflinks • Plotly and Cufflinks MODULE 4 Geographical Plotting • Introduction to Geographical Plotting • Choropleth Maps – Part 1 • Choropleth Maps – Part 2 • Choropleth Exercises • Projects using Analysis and Visualisation Machine Learning Part -4 Machine Learning: MODULE 1 Introduction to Machine Learning • What is Machine learing? • Overview about scikit-learn package • Types of ML • Basic steps of ML • ML algorithms • Machine learning examples MODULE 2 Data Preprocessing • Dealing with missing data • Identifying missing values • Handling with categorical data • Imputing missing values • Nominal and Ordinal features MODULE 3 Machine Learning Classifiers • K-Nearest Neighbors (KNN) • Decision tree • Random forest • Naive Bayes • Logistic Regression
- 5. State management • StateManagement with HTTP • Page Server • View State • User level • Session • Application Level • Application • Website • Cookies • Cleaning the session State • Global Application class (global. asax) • Web configuration file ( web.config) • Web Caching Intrinsic objects under Asp.Net • Request Object • Response Object • Session Object • Application Object • Server Object • View State object Advanced Asp.Net • Using FTP software • Using Browser • Creting Web Setup Project • Component Programming(Data Logic Layer) • Ajax LINQ • C# Language Extensions in 3.5 (Prerequisite) • ?Type Inference • ?Object Initializers. • ?Anonymous Types • Extension Methods • ?Partial Methods LINQ Architecture • Understanding the LINQ Framework • LINQ Providers • LINQ to Objects • LINQ to SQL • LINQ to Dataset • LINQ to XML LINQ to Objects • ?IEnumerable<T> and IQueryable<T> interfaces • System.Linq namespace • Query Expressions • Lambda Expression • Using Custom Class Collection LINQ to SQL • Defining the Data Model classes • ?Using Mapping attributes • Using the Data Context class • Defining Relationships using Associations • Creating a customized Data Context class
- 6. MODULE 4 RegressionBased Learning • Simple Regression • Multiple Regression • Predicting house prices with Regression MODULE 5 Clustering Based Learning • Definition • Types of clustering • The k-means clustering algorithm MODULE 6 Natural Language Processing • Install nltk • Tokenize words • Tokenizing sentences • Stop words with NLTK • Stemming words with NLTK • Twitter Sentiment analysis Project MODULE 7 Working with OpenCV • Installing opencv • Reading and writing images • Applying image filters • Writing text on images • Image Manipulations • Face detection Project • Speech Recognition Project Python with Data Science 1. Python with Data Science • Baric of Python Spider (Tool) • Introduction Spider • Setting Working Directory • Creating and Saving a Script file. • File Execution • Clearing Environment • Commentary Script File • variable creation • Arithmetic & Logical operator • Data type & associates operations 2. Data Structures • List • Tyler • Dictionary • Sets 3. Numpy • Array • Matrix and associated operation • Linear algebra & related operations 4. Panda data frame and data framerelated operations on Toyoter Corolla data sets • Reading File • Exploratory data Analysis • Data preparation and processing 5. Data visualization on Toyo to Corolla dataset using matplotlib and seaborne libraries • Scalther plot • Line plot • Bar plot • Historiography • Box plot • Pain plot
- 7. 6. Control Structureusing Toyota Corolla data sets • If else family • for loop • for loop with if break • While loop • Functions • CASE STUDY • Regression • predicting price of powered cares Clarification • Clarification personal income
- 8. A1/17, Top Floor,Opposite Metro Pillar no: 636, Main Najafgarh Road, Janakpuri New Delhi – 110058 011-4166-8088, 90155-96280, 9313565406 www.balujalabs.in Course Highlight 1.Consistent Classroom Guidance 2.Meticulously designed Study Material 3.Review of Previous years question papers 4.Regular model Mock tests on exam patterns 5.One on One attention 6.Time Bound Completion 7. Experienced full time faculty 8.Small batches 9.5 days a weekend batches 10.Weekly test 11.Accommodation for outstation students(PG)