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Data Science with Python
Rated 4.0/5 based on 24 Votes customer reviews

Data Science with Python Training

Learn to program with Python, how to create amazing data visualizations, and how to use Machine Learning with Python

  • 40 hours of Instructor-led sessions
  • Beginner to Advanced level
  • Hands-on learning
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Modes of Delivery

Key Features

40 hours of Instructor-led training sessions
Interactive hands-on learning sessions
Understand the core fundamental concepts of Data Science
Learn to use matplotlib and seaborn for data visualizations & other advanced features
Learn to program with Python & create amazing data visualizations
Our Data Science experts will guide students in implementing the technology for future projects

Description

Data science has been touted as being among the hottest professions in the market today making data scientists a revered class of professionals who are much valued by their organization.

Start your path to becoming a Data Scientist, using the power of Python to analyze data, create beautiful visualizations, and use powerful machine learning algorithms to convert your data into meaningful statistics. This comprehensive boot camp from Zeolearn aims at helping you understand the core concepts of Data science and the practical expertise needed to use it for data mining, visualization and information management. You will learn both theory and practical subjects by working on some of the most common and famous examples. We'll teach you how to program with Python, how to create amazing data visualizations, and how to use Machine Learning with Python! Enrol now and get started on this rewarding career that drives top businesses in the world today!

What you will learn!

  • Programming with Python
  • NumPy with Python
  • Using pandas Data Frames to solve complex tasks
  • Use pandas to handle Excel Files
  • Web scraping with python
  • Connect Python to SQL
  • Use matplotlib and seaborn for data visualizations
  • Use plotly for interactive visualizations
  • Machine Learning with SciKit Learn, including:
  • Linear Regression
  • K Nearest Neighbors
  • K Means Clustering
  • Decision Trees
  • Random Forests
  • Natural Language Processing
  • Neural Nets and Deep Learning
  • Support Vector Machines, and much, much more!
  • You will also learn many tools like Spark, Jupyter, Tensorflow etc.,

Is this course right for you?

This Data Science with Python training is well suited for both beginners who wish to pursue data science as a career and experienced data analysts who wish to get a comprehensive in-depth understanding of using Python for data analysis.

Prerequisites:

Python is a prerequisite for pursuing this course, but since this course is also aimed at beginners, we have included an additional module on Python basics that will help them come upto speed with other Data Scientists with just an additional 12 hours of learning.

Who Can Attend?

  • Analytics professionals who want to work with Python
  • Software professionals looking for a career switch in the field of analytics
  • IT professionals interested in pursuing a career in analytics
  • Graduates looking to build a career in Analytics and Data Science
  • Experienced professionals who would like to harness data science in their fields
  • Anyone with a genuine interest in the field of Data Science

Curriculum

  • Course Overview with Data Science
  • Environment Set-up and Installation –
  • Set up Anaconda, Jupyter, Ipython and install Python.
  • Set up an IDE - Option to choose from installing - PyCharm CE or Sublime or VIM or Emacs or VI
  • Jupyter Notebooks
  • Optional: Virtual Environments
  • Introduction to Python Crash Course
  • Python Crash Course - Part 1 - Basics
  • Python Crash Course - Part 2 - OOPS concepts
  • Python Crash Course - Part 3 - Modules
  • Python Crash Course - Part 4 - Final
  • Python Crash Course Exercises - Overview
  • Python Crash Course Exercises - Solutions
  • Introduction to Numpy
  • Numpy Arrays
  • Quick Note on Array Indexing
  • Numpy Array Indexing
  • Numpy Operations
  • Numpy Exercises Overview
  • Numpy Exercises Solutions
  • Introduction to Pandas
  • Series
  • Data Frames - Part 1 Introduction
  • Data Frames - Part 2 Organizing
  • Data Frames - Part 3 Set up
  • Missing Data
  • Group by
  • Merging Joining and Concatenating
  • Operations
  • Data Input and Output
  • Salaries Exercise Overview
  • Note on SF Salary Exercise
  • SF Salaries Solutions
  • E-commerce Purchases Exercise Overview
  • E-commerce Purchases Exercise Solutions
  • Introduction to Matplotlib
  • Matplotlib Part 1 Set up
  • Matplotlib Part 2 Plot
  • Matplotlib Part 3 Next steps
  • Matplotlib Exercises Overview
  • Matplotlib Exercises - Solutions
  • Introduction to Seaborn
  • Distribution Plots
  • Categorical Plots
  • Matrix Plots
  • Regression Plots
  • Grids
  • Style and Color
  • Seaborn Exercise Overview
  • Seaborn Exercise Solutions
  • Pandas Built-in Data Visualization
  • Pandas Data Visualization Exercise
  • Pandas Data Visualization Exercise- Solutions
  • Introduction to Plotly and Cufflinks
  • Plotly and Cufflinks
  • Introduction to Geographical Plotting
  • Choropleth Maps - Part 1 - USA
  • Choropleth Maps - Part 2 - World
  • Choropleth Exercises
  • Choropleth Exercises - Solutions
  • 911 Calls Project Overview
  • 911 Calls Solutions - Part 1
  • 911 Calls Solutions - Part 2
  • Finance Data Project Overview
  • Bank Data
  • Finance Project - Solutions Part 1 - Method 1
  • Finance Project - Solutions Part 2 - Method 2
  • Finance Project - Solutions Part 3 - Method 3
  • Link for ISLR
  • Introduction to Machine Learning
  • Machine Learning with Python
  • Linear Regression Theory
  • Model selection Updates for SciKit Learn
  • Linear Regression with Python - Part 1 Introduction
  • Linear Regression with Python - Part 2 Deep dive
  • Linear Regression Project Overview
  • Linear Regression Project Solution
  • Logistic Regression Theory - Introduction
  • Logistic Regression with Python - Part 1 - Logistics
  • Logistic Regression with Python - Part 2 - Regression
  • Logistic Regression with Python - Part 3 - Conclusion
  • Logistic Regression Project Overview
  • Logistic Regression Project Solutions
  • KNN Theory
  • KNN with Python
  • KNN Project Overview
  • KNN Project Solutions
  • Introduction to Tree Methods
  • Decision Trees and Random Forest with Python
  • Decision Trees and Random Forest Project Overview
  • Decision Trees and Random Forest Solutions Part 1
  • Decision Trees and Random Forest Solutions Part 2
  • SVM Theory
  • Support Vector Machines with Python
  • SVM Project Overview
  • SVM Project Solutions
  • K Means Algorithm Theory
  • K Means with Python
  • K Means Project Overview
  • K Means Project Solutions
  • Principal Component Analysis
  • PCA with Python
  • Recommender Systems
  • Recommender Systems with Python - Part 1 The Foundation
  • Recommender Systems with Python - Part 2 Deep Dive
  • Natural Language Processing Theory
  • NLP with Python
  • NLP Project Overview
  • NLP Project Solutions
  • Big Data Overview
  • Spark Overview
  • Local Spark Set-Up
  • AWS Account Set-Up
  • Quick Note on AWS Security
  • EC2 Instance Set-Up
  • SSH with Mac or Linux
  • PySpark Setup
  • Lambda Expressions Review
  • Introduction to Spark and Python
  • RDD Transformations and Actions
  • Neural Network Theory
  • Welcome to the Deep Learning Section!
  • What is TensorFlow?
  • Changes with TensorFlow
  • TensorFlow Installation
  • TensorFlow Basics
  • MNIST with Multi-Layer Perceptron
  • TensorFlow with ContribLearn
  • Tensorflow Project Exercise Overview
  • Tensorflow Project Exercise - Solutions

Frequently Asked Questions

According to a Forbes survey, Data Scientist was the best job to be pursued in 2016. This trend continues and according to experts will continue in the years to come because of the relevance of data and the benefits that can be had from it. The need to leverage big data is increasing every day in just about every industry with companies finding unique ways of accelerating productivity and capturing the market to stay on top of the competition. The role of a data scientist is one that is highly respected in the tech field and is an extremely rewarding career path. Expert data scientists are much in demand, as the analysis of the information stored in big data has become a key interpreter of competition and growth between firms.

Zeolearn academy recognizes this demand and hence brings this course that is aimed at giving you the expertise needed to mine, manage and extract data using the Python language. With our workshop you will be able to understand the language and leverage it to create outstanding programs for data interpretation and analysis. 

Zeolearn brings you mentor driven courses that not only helps professionals gain theoretical expertise but also the practical experience in a wide variety of courses including courses on Programming such as Scala and C#, which are very popular. The fact that our workshops are mentor driven gives us an edge over other training institutes since you can learn from industry experts about the application and challenges of upcoming technologies. We have so far trained thousands of professionals with the skills needed to land lucrative jobs and you could be next!

You will receive Data Science with Python certification in the form of a course completion certificate.

Towards the end of the course, all participants will be required to work on a project to get hands on familiarity with the concepts learnt. You will use Python to build your own programs, parse the Web and analyse and interpret data. This project, which can also be a live industry project, will be reviewed by our instructors and industry experts. On successful completion, you will be awarded a certification.

Classes are held on weekdays and weekends. You can check available schedules and choose the batch timings which are convenient for you.

You may be required to put in 10 to 12 hours of effort every week, including the classroom sessions/live class, self study and assignments.

We offer classes in classroom and online format. While classroom sessions are held in specific venues in your city, for online sessions all you need is a Windows computer with good internet connection and you can access the class anywhere, at anytime. A headset with microphone is also recommended.

You may also attend these classes from your smart phone or tablet.

Don’t worry, you can always access your class recording or opt to attend the missed session again in any other live batch.

This course is apt for existing and aspiring data analysts who wish to learn Python for Data Science.

One must have:

  • Macintosh (OSX)/ Windows(Vista and higher) Machine
  • Internet Connection

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