Data Science Course with Python

4.5 (6729) 9832 Learners

Data Science using Python programming certification course enables you to learn data science concepts from scratch. This Python Course will also help you master important Python programming concepts such as data operations,  file operations, object-oriented programming and various Python libraries such as Pandas, Numpy, Matplotlib which are essential for Data Science. Wissenhive's Python Certification Training course is also a gateway towards your Data Science career.

Duration
45+ Hours
Institution
Open Source
Language
English
Video Script
English

Overview

Python is a rapidly growing high-level programming language which enables clear programs on small and large scales. Its advantage over other programming languages such as R is in its smooth learning curve, easy readability and easy to understand syntax. With the right training Python can be mastered quick enough and in this age where there is a need to extract relevant information from tons of Big Data, learning to use Python for data extraction is a great career choice.

Our course will introduce you to all the fundamentals of Python and on course completion you will know how to use it competently for data research and analysis. Payscale.com puts the median salary for a data scientist with Python skills at close to $100,000; a figure that is sure to grow in leaps and bounds in the next few years as demand for Python experts continues to rise.

What you will learn

  • Programmatically download and analyze data
  • Learn techniques to deal with different types of data – ordinal, categorical, encoding
  • Learn data visualization
  • Using I python notebooks, master the art of presenting step by step data analysis
  • Gain insight into the 'Roles' played by a Machine Learning Engineer
  • Describe Machine Learning
  • Work with real-time data
  • Learn tools and techniques for predictive modeling
  • Discuss Machine Learning algorithms and their implementation
  • Validate Machine Learning algorithms
  • Explain Time Series and its related concepts
  • Perform Text Mining and Sentimental analysis
  • Gain expertise to handle business in future, living the present

Syllabus

International industry expertise at your disposal as you deep-dive into the research topic and sector of your choice.

Course Content

Introduction to Data Science (4 Lectures)

What is Data Science?

15:48 Play

Analytics Landscape

15:48 Play

Life Cycle of a Data Science Project

15:48 Play

Data Science Tools & Technologies

15:48 Play

Mastering Python (8 Lectures)

Python Basics

15:49 Play

Data Structures in Python

15:49 Play

Control & Loop Statements in Python

15:49 Play

Functions & Classes in Python

15:49 Play

Working with Data

15:49 Play

Analyze Data using Pandas

15:49 Play

Visualize Data

15:49 Play

Case Study

15:49 Play

Probability and Statistics (8 Lectures)

Measures of Central Tendency

15:50 Play

Measures of Dispersion

15:50 Play

Descriptive Statistics

15:50 Play

Probability Basics

15:50 Play

Marginal Probability

15:50 Play

Bayes Theorem

15:50 Play

Probability Distributions

15:50 Play

Hypothesis Testing

15:50 Play

Advanced Statistics & Predictive Modeling - (6 Lectures)

ANOVA

15:52 Play

Linear Regression (OLS)

15:52 Play

Case Study: Linear Regression

15:52 Play

Principal Component Analysis

15:52 Play

Factor Analysis

15:52 Play

Case Study: PCA/FA

15:52 Play

Advanced Statistics & Predictive Modeling -II (6 Lectures)

Logistic Regression

15:54 Play

Case Study: Logistic Regression

15:54 Play

K-Nearest Neighbor Algorithm

15:54 Play

Case Study: K-Nearest Neighbor Algorithm

15:54 Play

Decision Tree

15:54 Play

Case Study: Decision Tree

15:54 Play

Time Series Forecasting (7 Lectures)

Understand Time Series Data

15:55 Play

Visualizing Time Series Components

15:55 Play

Exponential Smoothing

15:55 Play

Holt's Model

15:55 Play

Holt-Winter's Model

15:55 Play

ARIMA

15:55 Play

Case Study

15:55 Play

Course Details

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. 

Wissenhive's  Python Certification Training not only focuses on fundamentals of Python, Statistics and Machine Learning but also helps one gain expertise in 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 and assignments and scenarios that help you gain practical experience in addressing predictive modeling problem that would either require 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. 

Furthermore, you will be taught of Reinforcement Learning which in turn is an important aspect of Artificial Intelligence. You will be able to train your machine based on real-life scenarios using Machine Learning Algorithms.

Who should go for the course?

  • Programmers, Developers, Technical Leads, Architects
  • Developers aspiring to be a ‘Machine Learning Engineer'
  • Analytics Managers who are leading a team of analysts
  • Business Analysts who want to understand Machine Learning (ML) Techniques
  • Information Architects who want to gain expertise in Predictive Analytics
  • 'Python' professionals who want to design automatic predictive models

 

Enquiry

Training Options

Self-paced Training

299
  • Lifetime access to high-quality self-paced eLearning content curated by industry experts
  • 3 simulation test papers for self-assessment
  • Lab access to practice live during sessions
  • 24x7 learner assistance and support

Live Virtual Classes

499
  • Online Classroom Flexi-Pass
  • Lifetime access 
  • Practice lab and projects with integrated Azure labs
  • Access to Microsoft official content aligned to examination

One on One Training

Enquiry Now
  • Customized learning delivery model (self-paced and/or instructor-led)
  • Flexible pricing options
  • Enterprise grade learning management system (LMS)
  • Enterprise dashboards for individuals and teams
  • 24x7 learner assistance and support

Exam & Certification

No exam required.

The candidate will complete a project at the end of the training. On successful completion of the project the candidate will receive a certificate of training.

Data Science Course with Python

Frequently Asked Questions

In 2012, Harvard Business Review dubbed Data Scientist the sexiest job of the 21st Century. Companies like Google, Facebook and others collect user data and sell them to ad companies to earn profits. How do you think they know whether you like dogs or cats? How do you think Amazon knows what products to recommend to you even when they haven’t explicitly asked you about it? The answer is data. Some other major reasons why data science is popular are:

  • Data-driven decision making is increasing in demand.
  • Due to the lack of well-trained data scientists, professionals trained in data science are offered the highest salary in the tech world.

The top skills that are needed to be a Data Scientist include the following:

  1. Python Coding
  2. R Programming
  3. Hadoop Platform
  4. SQL database and coding
  5. Machine Learning and Artificial Intelligence
  6. Apache Spark
  7. Data Visualization
  8. Unstructured data
  1. Python Coding: Python is one of the most common and popular programming languages used in the field of data science. Owing to the versatility as well as the simplicity that Python offers, it takes various formats of data and helps in the processing of this data. Python also allows data scientists to create datasets as well as perform various operations on a dataset.
     
  2. R Programming: Comprehensive knowledge of at least one analytical tool is preferred while embarking on a journey to become a master Data Scientist. Knowledge of R programming is usually an advantage for data scientists in order to make any data science problem easier to solve.
     
  3. Hadoop Platform: Strictly speaking, the Hadoop platform is not a requirement for data science, but is heavily preferred in several data science projects. A study of 3490 jobs on LinkedIn proves that Hadoop is still the leading skill requirement for a data science engineer.
     
  4. SQL database and coding: SQL is a language that is specifically designed to help data scientists to access, communicate as well as work on data. It helps a data scientist gain insights into the structure and formation of a database. MySQL also possesses concise commands that save time and decrease the level of technical skills required to perform operations on a database.
     
  5. Machine learning and Artificial Intelligence: Proficiency in the areas of Machine Learning and Artificial Intelligence is now a prerequisite for the pursuit of a career in Data Science. The knowledge and concepts of Machine Learning and Artificial Intelligence that a potential data scientist must be familiar with include the following:
    1. Reinforcement Learning
    2. Neural Network
    3. Adversarial learning
    4. Decision trees
    5. Machine Learning algorithms
    6. Logistic regression etc.
  6. Apache Spark: One of the most popular data sharing technologies worldwide, Apache Spark is a big data computation, not unlike Hadoop. The only difference between Apache Spark and Hadoop is that Apache Spark is faster, because of the fact that Hadoop reads and writes to the disk, whereas Spark makes caches of its computations in the system memory.Apache Spark, therefore, is a tool used to help the data science algorithms run faster. It also aids in the dissemination of data processing when dealing with a large data set as well as in the handling of complex unstructured data sets. Apache Spark also aids Data Scientists in preventing the loss of data. Its benefit also lies in the speed with which it operates, as well as the ease with which a data scientist can carry out a project.
     
  7. Data Visualization: A data scientist is expected to be able to visualize the data with the help of Visualization tools such as d3.js, Tableau, ggplot and matplotlib. These tools aid a data scientist in the conversion of complex results obtained as a result of processes performed on a data set and help to convert them into a format that is easy to understand and comprehend.Data visualization also gives organizations the opportunity to work directly with data. It also enables data scientists to quickly grasp insights from a particular data and outcome as well as enable them to act on the new outcome that is obtained.
     
  8. Unstructured data: It is important for a data scientist to be able to work with unstructured data, which is content that is not labelled and organized into database values. Examples of unstructured data include videos, social media posts, audio samples, customer reviews, blog posts etc.