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Learning Data Science with Python


Duration: 2 Days


Data from different systems that is accumulated over time possess capabilities to provide us with valuable insights helping us take important decisions. Knowing and understanding how you can draw these meaningful insights from a data set not only honours you as a “Data Scientist”, but also places you in one of the best jobs of our generation. In this course you will learn how the power of Python can be used with the concepts of Data Science to analyse data and solve few of the world’s most challenging questions. You will understand how to use various data structures of Python to gather the data, process it and visualize the result with libraries like Matplotlib. You will use Python to get your hands onto one of the best image processing libraries “OpenCV”. This course includes important insights on Natural Language Processing, String Transformation and how we can commit these capabilities using libraries like NLTK and Inflect. You will deal with popular scientific computing...Read more


  • Understand the life cycle of a Data Science implementation and how Python integrates in a Data Science eco-system
  • Learn to use different data structures in Python
  • Learn how to use Pandas with Python to handle, reshape and combine data
  • Use Matplotlib with Python for Data Visualization
  • Understand how to perform operations like slicing and indexing on Numpy arrays
  • Perform Image processing with OpenCV over python
  • Implement Natural Language Processing and String Transformation using NLTK and Inflect
  • Do complex computation using the SciPy library
  • Use Prophet to Forecast Time Series data

Who Should Attend

  • Developers / Architects seeking a career in Data Science
  • Professionals looking for a shift to Python
  • Candidates having passion to develop business acumen
  • Candidates looking to get certified in Data Science with Python


  • Basics of Python Programming

Course Outline

Day 1

    1. Introduction to Python Data Science Eco-System
      • Python Versatility: From Data Analysis to Web Crawling
      • Python Anaconda
      • Jupyter Notebooks
      • Data Science Libraries
    2. Pandas Data Frames
      • Data Frame Structure
      • Create Data Frames
      • Work with Rows and Columns within Data Frames
      • Perform Operations: MIN, MAX, STD, etc.
      • Conditional Selection
      • Understand set_index
    3. Handle Missing and Duplicate Data
      • Implications of Missing and Duplicate Data
      • Use fillna
      • Use dropna
      • Use interpolate
      • Use replace function
      • Use unique function
      • Use drop_duplicate function
    4. Combining Data
      • When to Combine Data Frames
      • Merging Data in Various Ways: Left, Right, Inner and Outer
      • Using join Function
      • Concatenate Data Vertically
      • Concatenate Data Horizontally
    5. Matplotlib – Data Visualization
      • Plotting Diagrams with Matplotlib
      • Using Matplotlib from within Pandas
      • Create Quality Diagrams
      • Visualise Data in Jupyter Notebooks
      • Other Visualization Libraries in Python
    6. Introduction to Arrays with Numpy
      • Understand Arrays: 1D, 2D and 3D
      • Use Array Computation in Real-Life
      • Create Various Types of Arrays
      • Save and Load Arrays
    7. Inspecting Arrays
      • Array Dimensions
      • Length of Arrays
      • Data Type of Array Elements
      • Convert the Type of Arrays
    8. Array Mathematics
      • Perform Arithmetic Operations: add, sub, mult and div
      • Perform Advanced Arithmetic Operations: sqrt, exp and sin
      • Element Wise and Array wise Comparison
      • Aggregation Functions: min, max, median, mean and standard deviation

Day 2

    1. Descriptive Statistics using SciPy
      • Create Uniform Distribution from the Given Data
      • Creating Discrete Distribution from the Given Data
      • Compute Geometric Mean Along with Specified Axis
      • Compute Kurtosis
      • Find Mode in the Data
      • Test the Skewness in the Data
      • Calculate Standard Error of Mean
    2. Web Data – Extraction and Pre-Processing
      • Data Extraction from Web Resources using urlib and beautifulsoup
      • Noise Removal from Text Data
      • Tokenization of Text Data
      • Normalizing Text Data
      • Perform NLP Tasks on Text Data using NLTK and Inflect
    3. Handle Time Series Data
      • Structure of Time Series Data
      • Set-up an Environment for Time Series Data Analysis
      • Using Prophet Library from Facebook
      • Forecast Time Series Data
    4. OpenCV for Image Processing
      • Introduction to Image Processing and Pixels
      • Libraries Used in Image Processing
      • Image Storing Methods: RGB, Greyscale and Binary
      • Work with Color Spaces
      • Draw Objects on Live Image
      • Image Transformations
    5. Projects
      • Process Healthcare Data for Patient Treatment Analytics
      • Data Pre-processing for Aircraft Maintenance
      • Create Live Sketch Solution for Kiosk Entertainment System Based on Live Camera Stream

About The Trainer


Arzan Amaria
Sr. Solutions Architect – Cloud and IoT

Arzan has more than 9 years of experience in Microsoft infrastructure technology stack, Data Science, Cloud and IoT. He has great amount of experience in deploying Cloud based solutions. He is a multi-cloud professional with exposure to Azure, AWS and other IIoT Cloud platforms like GE Predix and IBM Watson.

As a Cloud Solution Architect at CloudThat, he is an expert at deploying, supporting and managing client infrastructures on Azure. Having core training and consulting experience, he specializes in delivering individual training and corporate training on Azure. He is also engaged in extensive research and development in the field of IoT and Data Science and leads a team for the same. He has delivered trainings on IoT and is currently designing Cloud integrated solutions.

He has been training professionals for various Microsoft partners such as Wipro, HPE, HCL, Infosys, Accenture, TCS and many more in the recent past.

He holds following Certifications:

  • Microsoft Certified Trainer (MCT)
  • CTT+ (Certified Technical Trainer)
  • MCSD: Azure Solutions Architect
  • MCSE (Server Track)
  • MCTS in Machine Learning
  • VCA-DCV (Data Center Virtualization – Associate)
  • Microsoft Certified Specialist with Hyper – V Virtualization
  • AWS Certified Solutions Architect – Associate Level
  • CEH (Certified Ethical Hacker, EC Council University US)

MCT CompTIA Cloud Essentials Microsoft Certified Solutions Developer
MCSE Microsoft Certified Technology Specialist MCTS vmware certified professional data center virtualization
Microsoft Specialist Server Virtualization with Windows Server AWS Solutions Architect Associate Certified Ethical Hacker

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