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Analytics Using Python

Review (3)

20,000.00

Python has been one of the premier, flexible, and powerful open-source language that is easy to learn, easy to use, and has powerful libraries for data manipulation and analysis.

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12 Sessions
36 Hours



Skills you will master

Python Basics
Data Types & Variables
Conditional Statements
Iterations/ Looping
Numpy
Pandas
Matplotlib
Regression Modelling
KNN Classification
SVM
Naive Bayes
Random Forest
Decision Trees
Unsupervised Learning
Dimensionality Reduction
Model Selection
Boosting

UPCOMING BATCHES

03
Mar
Weekends
04:00 PM - 07:00 PM
07
Apr
Weekends
04:00 PM - 07:00 PM
05
May
Weekends
04:00 PM - 07:00 PM
Mob : +91-9810600764
Address : M8, Lower ground Floor, Sector 14 OLD DLF Gurgaon 122001
Email : info@palin.co.in

20,000.00

Category:
About The Program

Python has been one of the premier, flexible, and powerful open-source language that is easy to learn, easy to use, and has powerful libraries for data manipulation and analysis. For over a decade, Python has been used in scientific computing and highly quantitative domains such as finance, oil and gas, physics, and signal processing

During the training participants will be hands on

and will be going through

 

Career Advisor
Vinit Kumar, Consultant
Palin Delivers real world relevance with activities and assignments that helps students build critical thinking and analytic skills that will transfer to other courses and professional life.
Program Description
Python Certification Training not only focuses on fundamentals of Python, Statistics, Machine Learning and Spark but also helps one gain expertise on applied Data Science at scale using Python. The training is a step by step guide to Python and Data Science with extensive hands on. The course is packed with several activity problems, quiz and assignments and scenarios that help you gain practical experience in addressing an automation problem that would either require only Python or Machine Learning using Python. Starting from basics of Statistics such as mean, median and mode to exploring features such as Data Analysis, Regression, Classification, Clustering, Naive Bayes, Cross Validation, Label Encoding, Random Forests, Decision Trees and Support Vector Machines with a supporting example and exercise help you get into the weeds.
Curriculum
Click Here to Download

   Session 1 - Getting Started with Python

Introduction to the Class & Course Objectives,Introduction to Python,Scope of Python, Why Python over R/SAS, Setting up the Machine

   Session 2 - Data Structures and Data Handling & String

Python Basics, Data Types and Variables (Integers, Floats, Strings, Lists, Dictionaries, Sets and Tuples), Conditional Statements (If, If-else, Nested if-else), Iteration/Looping ( For while, Nested Loops), Functions (Writing reusable code), Exception Handling in Python

   Session 3 - Working with Files & Databases

Reading and Writing common file formats, Extracting and Storing data from Databases

   Session 4 - Machine Learning Prerequisites

Numpy (Lists vs. Arrays, Vectors and Matrices, Dot Products and Matrix Products, Universal Array Functions), Pandas (Series and DataFrames, Columns and Indexes, Summary Statistics, The apply() function), Matplotlib (Box plots, Line Chart, Scatterplot, Histograms)

   Session 5 - Practice Session

Practice to get hands on Numpy, Pandas, & Matplotlib

   Session 6 - Introduction to Machine Learning

What is Machine Learning, Applications of Machine Learning, Setting up the working environment, Data Processing (Importing the Dataset, Handling missing data, Handling Categorical data, Splitting the dataset into training and test dataset, Feature Scaling)

   Session 7 - Introduction to Regression Modelling

Simple Linear Regression, Multiple Linear Regression,Polynomial Regression,Support Vector Regression

   Session 8 - Classification

Logistic Regression, K-Nearest Neighbors (K-NN), Support Vector Machines, Kernel SVM, Naive Bayes Classifier, Decision Tree Classification, Random Forest Classification

   Session 9 - Case Study I

TITANIC - The Sinking Ship

   Session 10 - Case Study II

Election Analysis, Stock Market Analysis

   Session 11 - Unsupervised Learning

Clustering – Intuition, K-Means Clustering, Hierarchical Clustering

   Session 12 - Dimensionality Reduction and Model Selection

Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Kernel PCA, Model Selection & Boosting( Introduction, What is Overfitting, Bias Variance Tradeoff, K-Fold Cross Validation, Grid Search) (Bonus Lecture) XGBoost in Python

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Program Highlights
Instructor-led
Instructor led training is the live and interactive training by the industry professionals
Live Case Study
A project undertaken by you in supervision of the trainers will be conducted
Assignments
Regular assignment leads to a proper revision of your skills.
Lifetime Access
Access to all the recording for the lifetime and avoid notes maintaining chios.
24 X 7 Support
Consultants are available online for proper guidance about the course.
Certification
After successful completion of the training and case study Palin will certify you as Palin certified Data Analyst.
Career Counselor
Avail career guidance and Professional guidance for resume building, unlimited opportunities and interviews.
Certification Preview
Trainers

Highly motivated with leadership skills having Master’s Degree in Statistics. Holds good working experience in different domains like Retail, Banking, Healthcare FMCG, Marketing analytics using SAS, R, Python, STATA.
FAQ's
   Who can go for Python Analytics?
There is a booming demand for skilled data scientists and machine learning with Python professionals across all industries that make this course suited for participants at all levels of experience. We recommend this Python course especially for the following professionals
   What is Python Analytics?
Python has been one of the premier, flexible, and powerful open-source language that is easy to learn, easy to use, and has powerful libraries for data manipulation and analysis. For over a decade, Python has been used in scientific computing and highly quantitative domains such as finance, oil and gas, physics, and signal processing.
   What is the future of Python Analytics?
When it comes to data science, Python is a great language to master. First of all, it’s a language friendly to beginners — there are certain features in the language that make it easy to get started and develop prototypes quickly.
   Pre-requisites for Python Analytics?
There are no hard pre-requisites. Basic understanding of Computer Programming terminologies is sufficient. Also, basic concepts related to Data analysis are beneficial but not mandatory. You will also get familiar with basics of statistics during the course
   Is this a classroom training or online?
Palin Analytics offers you both the training modes. You can opt either classroom or online.
   Which one is better Online or Classroom?
Both the training modes are good to get trained. But online training is more better than the classroom training because online trainings are live interactive where you can raise your concerns at any point during the session. Additionally recording of the same session will be provided which you can access anytime anywhere.
   What all topics will be covered in Python Analytics?
Python Basics, Working with Files & Databases, Machine Learning Prerequisites (Numpy, Pandas, Matplotlib), Data Processing, Regression Modeling, KNN, SVM, Naive Bayes, Decision Trees, Random Forest, Unsupervised Learning - Clustering, Dimensionality Reduction, Model Selection & Boosting
   After the course for which companies I can apply for?
Almost every organisation which has a large customer base is shifting to Python Analytics. Companies like Amazon, Infosys, TCS, Accenture, IBM, Flipcart, Airtel are highly in need for analytics professionals.
   What are pay packages for fresher in Python Analytics?
An Entry-Level Data Scientist, IT earns an average salary of Rs 579,714 per year. The highest paying skills associated with this job are Machine Learning, Python, and SQL.
   What is the process for Python Analytics?
We'll be starting from python basics to understand working with files and databases. Then we'll cover machine learning things like numpy, pandas & Matplotlib and advance statistical techniques to get hands on business analytics with regression modeling, classification & unsupervised learning.
Reviews

Reviews

    Best placement assistance with advanced learning technologies in data science and machine learning. Thank you palin !!!

    Specialised in Analytics, Rightly they say !!
    Strongly Recommended Palin!!! 🙂 (y)

    Best training for advanced learning into analytics. Best trainers for Python Analytics, Machine Learning and Data Science.

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