Analytics With Python


Learn this powerful object oriented programming language with its dedicated library for data analysis and predictive modeling for data munging, data engineering, data wrangling, website scraping, web app building, etc.

Data Science with Python

At Kovid Academy, the training curriculum will cover all the stages of Python Data Science from Data extraction, munging, cleansing, modelling and visualization. This course is comprehensively designed to discuss the core concepts related to Python by laying specific focus on the Data Science. The training curriculum allows the participants to have a clear understanding of the data science, statistical concepts relevant to data science, programming using the python, machine learning concepts and in-depth knowledge on the implementation of libraries and models to solve real world data science challenges.

Key concepts of Python programming using the Python libraries

This course is designed to meet all the industry standards and the best practises that gives the participants a hands-on practice on the different application libraries like Pandas, NumPy, SciPy, Scikit-learn, and Matplotlib.

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Course Details

After the completion of this course, you will gain expertise in the following concepts:

  1. Determining what Data Science is all about and the role that Python plays in it
  2. Identifying the phases of Data Analytics workflow
  3. Understanding the key statistical concepts relevant to Data Science
  4. Determining the Python environment and libraries
  5. Getting data from open data sources and web
  6. Determining the key concepts of Python programming
  7. Using the Python libraries like Pandas, NumPy, SciPy, Scikit-learn, and Matplotlib
  8. Implementing the Data Science workflows
  9. Machine Learning algorithms
  10. Integrating the Python code with Hadoop and Spark
Instructor Led training 24 Hrs
Instructor Interaction Yes
Live Support Post Training 1 Year
Simulated Projects 2
Capstone/Hands On/Real Time Project 2
Kovid Academy Data Science with Python Certificate Yes
33 CEU/PDU certificate Yes

Module 1: Introduction to Python Programming

  • Basic Operations in Python
  • Variable Assignment
  • Functions: in-built functions, user defined functions
  • Condition: if, if-else, nested if-else, else-if

Module 2: Data Structures in Python

  • List: Different Data Types in a List, List in a List
  • Operations on a list: Slicing, Splicing, Sub-setting
  • Condition(true/false) on a List
  • Applying functions on a List
  • Dictionary: Index, Value
  • Operation on a Dictionary: Slicing, Splicing, Sub-setting
  • NumPy Array: Data Types in an Array, Dimensions of an Array
  • Operations on Array: Slicing, Splicing, Sub-setting
  • Conditional(T/F) on an Array
  • Loops: For, While

Module 3: Statistics for Data Science

  • Seabourn & Matplotlib – Introduction
  • Univariate Analysis on a Data
  • Find the distribution
  • Find mean, median, mode and standard deviation of the Data
  • Multiple data with different distributions
  • Bootstrapping and sub-setting
  • Making samples from the Data
  • Making stratified samples – covered in bivariate analysis
  • Find the mean of sample
  • Central limit theorem
  • Plotting
  • Hypothesis testing + DOE
  • Bivariate analysis
  • Correlation
  • Scatter plots
  • Making stratified samples – covered in bivariate analysis
  • Categorical variables
  • Class variable

Module 4: Python Pandas

  • File I/O
  • Series: Data Types in series, Index
  • Data Frame
  • Series to Dataframe
  • Re-indexing
  • Operations on Data Frame: Slicing, Splicing (also Alternate), Sub-setting
  • Pandas
  • Stat operations on Data Frame
  • Reading from different sources
  • Missing data treatment
  • Merge, join
  • Writing to file
  • DB operations

Module 5: Data Migration, Munging, Aggregation & Visualisation

  • Data Aggregation, Filtering and Transforming
  • Lambda Functions
  • Apply, Group-by
  • Map, Filter and Reduce
  • Visualization
  • Matplotlib, pyplot
  • Seaborn
  • Scatter plot, histogram, density, heat-map, bar charts

Module 6: Linear Regression

  • Linear Regression: Lasso, Ridge
  • Variable Selection
  • Forward & Backward Regression

Module 7: Logistic Regression

  • Logistic Regression: Lasso, Ridge
  • Naive Bayes

Module 8: Unsupervised Learning

  • Unsupervised Learning – Introduction
  • Distance Concepts
  • Classification
  • k nearest
  • Clustering
  • k means
  • Multidimensional Scaling

Module 9: Random Forest

  • Decision trees
  • Cart C4.5
  • Random Forest
  • Boosted Trees
  • Gradient Boosting

Module 9: Random Forest

  • Decision trees
  • Cart C4.5
  • Random Forest
  • Boosted Trees
  • Gradient Boosting

Module 10: Support Vector Machines

  • Support Vector Machines
  • Hyper-plane to segregate to classes
  • Hyper-plane to segregate to classes
  • Gamma

Module 11: Recommenders

  • Recommendation engines
  • Collaborative filtering
  • Content based filtering

Module 12: Kaggler Project

  • Take your skills to Kaggle
  • Participate in competitions
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Why Data Science and Why Python?

According to Forbes, Data Science is one of the sexiest jobs of 21st century. The number of problems that can be solved using the data science are endless. As a matter of fact, there is a massive shortage of data scientists in the current market.
On the other hand, Python is considered as one of the most popular tools used by the Data Analysts and Data Scientists. It’s extremely capable programming language with a simple construct makes it one of the most vital tools in the repository of the Data Scientists.

What are the prerequisites of this course?

To make the most of this course, the participants are required to have Basic familiarity with the computing and programming concepts and Good knowledge of mathematical and statistical concepts

Who is the right candidate for this course?

This course is extensively useful for the aspirants who are looking for a career progression in the field of Data Science and also who have the designations including (but not limited) to – Technical Analyst, Data Analyst, Database Developer, Hadoop Developer, Big Data Architect, Programmer Analyst, Big Data Engineer, Business Analyst (Technical) etc.

What are the training materials provided?

For all the training modules that are covered in this course, adequate materials and good references will be provided to the participants. In the case of online interactive trainings, every session will be recorded and uploaded in the LMS, giving the participants a feasibility to recap their completed training sessions.

Is Certification offered and if so, how do you earn?

After the completion of course, the participants will undergo a certification examination. Based on their performance in the assignments, projects and the final examination, certificates will be issued to the participants.

What are the system requirements for participants?

The participants are recommended to have i3 or higher range processor with virtualization support and a minimum of 4 GB RAM (8 GB is recommended), 64-bit operating system and about 100 GB of free hard disk space is required.

How many hours is a student expected work?

This extensively depends upon the prior experience levels and the grasping nature of the participants. It means, the time period may vary from one participant to another. On an average, we have noticed that the participants are required to spend double the training hours. Let us consider, if the training is for 10 hours, then the participants are required to spend an additional of 20 hours more.