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COMPLETE PYTHON FOR DATA SCIENCE AND CLOUD COMPUTING

Python and Data Science Professional Training

Technology Learners

Regular Offline and Online Live Training

Week Days and Week Ends

Daily 2 hrs during Weekdays

•Build and deploy web applications Python and Data Science.

•Learn how to use and interpret Python and Data Science.

•Become a better developer by mastering Python and Data Science fundamentals

•How to connect to multiple data sources with Python and Data Science.

•Write Compile and Run codes and apps using Python and Data Science.

•The best way to learn modern Python and Data Science step-by-step from scratch.

•Learn Python and Data Science the fast track way with hands on teaching

•Learn the basics of Python and Data Science and get up and running quickly

•Amazing Step by Step Guide for Beginners to Learn Python and Data Science Language Quick and Simple!

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•You Get Real Time Project to practice

•25+ projects for good Learning experience

•Real time live project training and Guidance

•Personal attention and guidance for every student

•We Also provide Case studies for Online Training Courses

•100% Guaranteed Placements Support in IT Companies with Big Salaries

•Curriculum based on course outlines defined by in-demand skills in Python.

• Our dedicated HR department will help you search jobs as per your module & skill set, thus, drastically reducing the job search time

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•CNC Engineer, Software Developer, Testing Engineer, Implementation, Core Java, Struts, hibernate, Asp.net, c#, SQL Server, CNC Programming, backFront End, Javascript, Computer Graphics, Html, Css, Problem Solving, CSS, Web Technologies, Design, Software Development, Full Stack Developer

•Java, .Net, Selenium, QTP, DBA, PHP, Neoload, Manual Testing, Rest, Soap, Web Services, SQL, UI, Peoplesoft, Cloud

•Python, Django, Automation Testing, Cloud Computing, Aws, Java, J2ee, Web Services, Soap, Rest

•ux, ui, Python Developers, Qa Automation, sales, Ui Development, Ux Design, Software Development, Python, Qa Testing, Automation Testing

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Python Fundamental

•Python environment and versions

•Download lecture materials

•Install Anaconda

•Demonstrate Jupyter notebook

•Demonstrate Spyder

•Your first homework

•Data objects in Python (1)

•Data objects in Python (2)

•Data objects in Python (3)

•Demonstrate programming for data objects

•Understand String and operations

•Demonstrate programming for String objects (1)

•Demonstrate programming for String objects (2)

•Scalar variables and operations

•Understand date and time objects

•Demonstrate examples of date and time objects

•Comments in Python

•Demonstrate examples of comments in Python

•Learn tuples objects in Python

•Demonstrate tuple examples

•Learn list objects in Python

•Demonstrate list examples (1)

•Demonstrate list examples (2)

•Demonstrate list examples (3)

•Demonstrate list examples (4)

•Demonstrate list examples (5)

•Understand dictionary objects

•Show use cases about dictionary objects

•Introduce set objects

•Demonstrate programming on Set objects

•Control flow structure in Python

•User Defined Functions (UDF)

•Demonstrate examples of UDF

•Create Python packages

•Demonstrate how to create Python packages

•File input and output in Python (1)

•File input and output in Python (2)

•Introduce Iterators and generators

•Learn error handling in Python

•Introduce assert statement

•Object Orientated Programming (OOP) in Python

•Demonstrate use case of OOP (1)

•Demonstrate use case of OOP (2)

•Demonstrate use case of OOP (3)

•Homework of Python fundamental

•Python Numpy for Data Science

•Introduce Python Numpy

•Introduce Python Numpy (2)

•Create Numpy arrays (1)

•Create Numpy arrays (2)

•Create Numpy arrays (3)

•Create Numpy arrays (4)

•Introduce multi-dimensions Numpy arrays

•Learn properties of Numpy arrays

•Slicing Numpy arrays (1)

•Slicing Numpy arrays (2)

•Show cases of Numpy arrays

•Use array to slice Numpy arrays

•Transpose Numpy arrays

•Merge or stack Numpy arrays

•Introduce useful functions of Numpy arrays

•Data processing functions of Numpy arrays (1)

•Data processing functions of Numpy arrays (2)

•Data processing functions of Numpy arrays (3)

•Data sampling and generation

•Load and write data using Numpy

•Introduce first homework of Numpy

•Introduce second homework of Numpy

•Python Pandas for Data Science

•Introduce series objects

•Overview of Pandas

•Create Pandas data frames

•Show examples of creating Pandas data frames

•Read external files into data frames (1)

•Read external files into data frames (2)

•Demonstrate examples of reading external files

•Data conversion in data frames (1)

•Data conversion in data frames (2)

•Arithmetic operations of data frames

•Slicing data frames (1)

•Slicing data frames (2)

•Show examples of slicing data frames (1)

•Show examples of slicing data frames (2)

•Manipulate data frames (1)

•Manipulate data frames (2)

•Manipulate data frames (3)

•Manipulate data frames (4)

•Sort and rank data frames (1)

•Sort and rank data frames (2)

•Combine data frames

•Demonstrate examples of combining data frames

•Indexing methods in data frames

•Reshape data frames

•Treat missing values in data frames (1)

•Treat missing values in data frames (2)

•Treat missing values in data frames (3)

•Treat duplicated values in data frames

•Summarize data using Pandas data frames (1)

•Summarize data using Pandas data frames (2)

•Categorical data analysis (1)

•Categorical data analysis (2)

•Categorical data analysis (3)

•Categorical data analysis (4)

•Categorical data analysis (5)

•Categorical data analysis (6)

•Access other data sources

•Access SQLite with Python (1)

•Access SQLite with Python (2)

•Scrape web site data with Python

•Test data scraping with Python Pandas

•First homework of Pandas

•Second homework of Pandas

•Introduce MongoDB and work with Python

•Install MongoDB

•Programs: Interact Python with MongoDB (1)

•Programs: Interact Python with MongoDB (2)

•Data Visualization with Python

•Graph with Matplotlib and examples (1)

•Graph with Matplotlib and examples (2)

•Introduce and install Seaborn

•Demonstrate data visualization with Seaborn (1)

•Demonstrate data visualization with Seaborn (2)

•Introduce and install ggplot

•Demonstrate data visualization with ggplot

•Introduce and install plotly

•Demonstrate data visualization with offline plotly (1)

•Demonstrate data visualization with offline plotly (2)

•Demonstrate data visualization with online plotly (1)

•Demonstrate data visualization with online plotly (2)

•Statistical Analysis and Modeling with Python

•Introduce statistical tests

•One sample and two samples tests (1)

•One sample and two samples tests (2)

•Real world case: two samples tests

•Non-parametric tests with Python

•Multiple groups tests – ANOVA (1)

•Multiple groups tests – ANOVA (2)

•Multiple groups tests – ANOVA (3)

•Multiple groups tests – ANOVA (4)

•Case study for ANOVA with Python

•Introduce interaction by examples

•Work with interaction in ANOVA with Python

•Statistical tests with repeated measures

•Different types of pair tests

•Statistical tests for categorical data

•Chi-Square test

•Proportion test

•Homework & solutions to statistical tests with Python

•Linear regression and application (1)

•Linear regression and application (2)

•Linear regression and application (3)

•Linear regression and application (4)

•Feature engineering in modeling

•Feature selection in modeling

•Python codes for feature engineering

•Logistic regression and application (1)

•Logistic regression and application (2)

•Logistic regression and application (3)

•Logistic regression and application (4)

•Logistic regression and application (5)

•Logistic regression and application (6)

•Logistic regression and application (7)

•Logistic regression and application (8)

•Logistic regression and application (9)

•Logistic regression and application (10)

•Logistic regression and application (11)

•Logistic regression and application (12)

•Logistic regression and application (13)

•Use cases of statistical models (1)

•Use cases of statistical models (2)

•Use cases of statistical models (3)

•Use cases of statistical models (4)

•Use cases of statistical models (5)

•Use cases of statistical models (6)

•Use cases of statistical models (7)

•Use cases of statistical models (8)

•Use cases of statistical models (9)

•Use cases of statistical models (10)

•Use cases of statistical models (11)

•Introduce homework of statistical models

•Introduce homework of fraud detection project

•Data Science & Machine Learning Capstone Projects with Python

•Introduce project: predict online product sales

•Explain Python codes for predicting online product sales (1)

•Explain Python codes for predicting online product sales (2)

•Explain Python codes for predicting online product sales (3)

•Explain Python codes for predicting online product sales (4)

•Introduce project: credit risk analysis – develop score cards

•Lecture on Python program for credit risk analysis (1)

•Lecture on Python program for credit risk analysis (2)

•Lecture on Python program for credit risk analysis (3)

•Lecture on Python program for credit risk analysis (4)

•Lecture on Python program for credit risk analysis (5)

•Lecture on Python program for credit risk analysis (6)

•Lecture on Python program for credit risk analysis (7)

•Lecture on Python program for credit risk analysis (8)

•Lecture on Python program for credit risk analysis (9)

•Lecture on Python program for credit risk analysis (10)

•Explain project: measure sales promotion Progra

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