Best Python Training in Bangalore

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Best Python Training courses in Bangalore

Get the best Python training in Bangalore. Apponix is proud to say that its is one of the top Python training provider in Bangalore, we provide best training experience and results to our valued students, All our trainers are very experienced IT professionals and love to share their practical knowledge with the students.

Python Programming Training course is designed to suit all levels of students to provide in depth knowledge about python scripting. All classes will be conducted by IT Industry experts who will guide you through out the course to leverage Python scripting to make you get ready for your dream job.

Best Python Training institute in Bangalore

Training in Apponix technologies mainly focuses on the present scope of python and real time requirements which will introduce a new learning experience to the newbies. The pattern of the course structure meticulously designed for beginners and professionals who wanted to start or empower their skills on Python.

Every section of module and the code test conducts on every python concept will boost your coding skill. Real time projects like scraping a website or automating a daily repetitive tasks with your knowledge helps you to get interest and learn more. On successful completion of the course, you will step out with 100% satisfaction and knowledge to achieve your goals.

Python training course covers an up to date and most relevant topics which are required by most of the companies, our python instructor knows very well on topics & syllabus to be covered.

Why you should learn Python Programming?

Below are the few reasons to choose python:

  • Python has very simple syntax which is easy to understand
  • Millions of jobs opportunities for python developers.
  • Python is the most preferred language for Artificial Intelligence, Robotics, Web Development and DevOps.
  • Python is one of the most premier, flexible, and powerful open-source language that is easy to learn.
  • Python is easy to use, and has powerful libraries for data manipulation and analysis.
  • For more than a decade, Python has been used in scientific computing and highly quantitative domains such as finance, oil and gas, physics, and signal processing as of today.

Python Training Classes in Bangalore course objectives:

Main objectives of the course are as follows:

  • Write python scripts, unit test code
  • Programmatically download and analyse data
  • Learn techniques to deal with different types of data i.e. ordinal, categorical, encoding
  • Learn data visualisation

What are the prerequisites for Python Training?

A computer basics is good to start with & most important is you need to have good interest in learning python.

Why Should you choose Apponix as a Top Python Training centre?

  • Apponix has highly experienced and qualified Python instructors.
  • Till today we have 100% student satisfaction rate.
  • More than 1000 students are rated us as best training institute in Bangalore for python.
  • Well-equipped lab facility, Decent infrastructure.
  • All classrooms are Air-Conditioned.
  • All students are provided an individual laptop throughout the course with high-speed WiFi.

100% Placement assistance

Due to high demand for python developers and Python is great for web development. As there are lacks of jobs available in Bangalore city only there is a Hugh demand for python developers also.

Apponix has tied up with many recruitment agencies & companies also we are official partner of www.talentsarena.com, we have a dedicated Human Resource team who will constantly working with recruitment agencies for any new jobs opportunities and the dynamic team will keep you updated with all new job openings.

In other hand our trainers are very helpful & they will help you with most expected interview questions & answers in Python.

Python Training in Bangalore.Python, that's used by thousands of people to do things from testing microchips at Intel, to powering Instagram, to building video games with the PyGame library. It is the fourth most popular language according to an IEEE survey, behind old classics Java, C, and C++? So in celebration of our two new Python. Learning Python is very important in this era. You want to learn Python in a Real Time Manner with Practical Examples; you are in a right Track!

DATA SCIENCE WITH PYTHON

Introduction to Data Science
  • What is Data Science? – Introduction
  • Roles and Responsibilities of a Data Scientist
  • Life cycle of Data Science project
  • Tools and Technologies used
PART – A: STATISTICS FOR DATA SCIENTIST

Module 1: Introduction to Statistics

  • Types of Data
  • Data Measurement Scales
  • Fundamentals of Probability
  • Bayes Theorem

Module 2: Descriptive Statistics

  • What id Descriptive Statistics
  • Measure of Central Tendency (Mean, Mode and Median)
  • Measure of Dispersion/Spread (Range, Variance and Standard Deviation)

Module 3: Inferential Statistics

  • What is Inferential Statistics
  • Types of Sampling Techniques
  • Probability Sampling
  • Non Probability Sampling
  • Central Limit Theorem

Module 4: Probability Distributions

  • Types of Probability Distributions
  • Binomial Distribution
  • Poisson Distribution
  • Hyper Geometric Distribution
  • Normal Distribution
  • Z Distribution
  • T distribution

Module 5: Hypothesis Testing

  • What is Hypothesis Testing
  • Types of Hypothesis Testing
  • Parametric Hypothesis Testing
  • 2 Independent Samples T test
  • Paired T test
  • ANOVA (Analysis of Variance)
  • Chi – Square test for Independence
  • Chi – Square test for Goodness of Fit
  • Non Parametric Hypothesis Testing
  • Mann – Witney U Test
  • Wilcoxon Signed Rank Test
  • Kruskal – Wallis Test
  • Types of Errors (Type | and Type || Errors)
  • Co-variance, Correlation and Regression
PART – B: PYTHON FOR DATA SCIENTIST

Module 1: Python Programming

  • Introduction to Python with Anaconda Distribution
  • Introduction to Jupiter Notebook
  • Crash Course on Python Programming
  • Types of Operators
  • Python Data Types
  • List
  • Tuple
  • Dictionary
  • Sets
  • Data Types Operations & Methods
  • Flow Controls
    • If…..Else Statements
    • If ….Elif ….Else Statements
    • For Loops
    • While Loops
  • Functions
  • List Compressors
    • Lambda, Map and Filter

    Module 2: Introduction to Essential Python Libraries for Data Science

    • Numpy, Pandas, Matplotlib, Seaborn and Scikit Learn Libraries

    Module 3: Numpy

    • Introduction to Numpy Library
    • Numpy Arrays
    • Numpy Indexing and Selection
    • Numpy Operations

    Module 4: Pandas

    • Introduction to Pandas Library
    • Pandas Series and Data Frames
    • Pandas Indexing and Selection
    • Pandas Operations

    Module 5: Data Mugging / Wrangling with Pandas

    • Handling Missing Data
    • Group by Method
    • Merging, Joining and Concatenating Data Frames.
    • Pivot Table
    • Reshaping the Data Frame
    • Cross Tab / Contingency Table

    Module 6: Data Visualization

    • Various types of Plots and their Applications
    • Introduction to Matplotlib Library
    • Creation of plots
    • Plot Styles
  • Introduction to Seaborn Library
    • Distribution Plots
    • Categorical Plots
    • Matrix Plots
    • Regression Plots
  • Pandas Built-in Visualizations
  • PART – C: Machine Learning

    Module 1: Data Pre-processing Techniques

    • Sanity Checks
    • Missing Value Detection and Treatment
    • Outlier Detection and Treatment
    • Variable Transformation Techniques
    • Exploratory Data Analysis
    • Uni-Variate Analysis
    • Bi-Variate Analysis

    Module 2: Machine Learning Basics

    • Types of Machine Learning Techniques
    • Steps Followed in ML Model Building
    • Train set, Validation set and Test set
    • Bias and Variance Trade-off Study

    Module 3: Supervised Machine Learning Models

    • Linear Regression
    • Simple Linear Regression
    • Multivariate Linear Regression
  • Logistic Regression
  • Decision Tree
  • Support Vector Machine
  • k Nearest Neighbours (kNN)
  • Naïve Bayes
  • Module 4: Unsupervised Machine Learning Models (Clustering)

    • Types of Clustering Algorithms
    • k Means Clustering Algorithm
    • Hierarchical Clustering Algorithm
    • Evaluation of Clustering Techniques

    Module 5: Dimensionality Reduction Techniques

    • PCA (Principal Component Analysis)
    • LDA (Linear Discriminate Analysis)

    Module 5: Other Topics

    • Bagging Methods
    • Random Forest
  • Boosting Methods
    • Ada Boost (Adaptive Boosting)
    • XG Boost
  • Cross Validation Techniques (Pre-processing step)
    • K-Fold Cross Validation
    • Stratified K Fold Cross Validation

    Module 6: Interview Questions Discussions

    Loops and Decision Making
    • if statements
    • ..else statements
    • nested if statements
    • while loop
    • for loop
    • nested loops
    • Loop Control Statements
    • 1) break statement
    • 2) continue statement
    • 3) pass statement