Professional certification in Data Science, Python

PYTHON DATA SCIENCE TRAINING

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Duration & Study Mode:
Full time, Part-time, Evening and Weekends, Virtual online
Location:
London, Flexible online
Duration: 5 Days / 5 Weeks

Data Science is an interdisciplinary field that brought other branches like algorithms, processes, scientific methods, and effective processes to extract knowledge and insights from noisy, scattered, unstructured, and structured data. To do so, Data Scientists apply their knowledge, actionable insights, and skills from data over a wide range of application domains. This is how and where Python’s role is, a robust, interpreted, high-level, and general-purpose programming language. In this training, you will discover why scientists worldwide prefer the ‘Python’ programming language to deal with the ‘Data Science’ issues. Python is easy to learn and more scalable compared to other high-level programming languages. At the beginning of this training, you will learn how to create arrays from Python lists and build structured arrays. Then you will explore how Python is deployed for Data Science. Progressively, you will learn how to customize colour bars, Matplotlib, ticks, and plot legends. You may choose one among several online learning portals to join such training/courses on Python and Data Science.

Prerequisites

  • Formally, no prerequisites are mentioned to attend this course
  • It is expected that you are familiar with at least one or more high-level programming language
  • You can get the most out of this training if you have the experience of working as a Data Scientist or Data Analyst

What will you gain after this course

  • Python is an easy to learn programming language, and you can even learn the advanced level of Python without having any prior coding experience.
  • In the field of Data Science, you have to deal with a large chunk of data frequently. Using Python, you can ensure faster development and processing to extract valuable insights from Data Analysis.
  • Python programming language comes with many powerful packages for data science like SciPy, Pandas, NumPy, PyBrain, etc.
  • You can enjoy strong community support while using the Python programming language for Data Science.
  • It is now possible to build better Data Visualization effortlessly using Python. 
  • Hadoop is another most widely used platform for Big Data. Hadoop is also compatible with Python, and you can access the Hadoop API with the help of the Python package known as PyDoop.

Jobs you can get
with a PYTHON DATA SCIENCE TRAINING Certification

  • Data Scientist
  • Pyspark Data Scientist
  • Data Engineer (Python, SQL)
  • Associate Data Quality Analyst
  • Data Science Analyst – level I

Corporate Group Training

  • Customized Training
  • Onsite / Virtual
  • Instructor-led Delivery
  • For small to large groups

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Who is this certification for?

  • Anyone willing to learn and know about the process of integrating Python into Data Science may attend this training.
  • IT Professionals who are eager to serve the industry as Data scientists may also consider joining this training
  • Data Analysts, ML (Machine Learning) Specialists, and AI (Artificial Intelligence) Experts can also enhance their skills and knowledge by attending this training.
  • This training is well designed for Software Developers, Project Managers, Business Intelligence Managers, and Big Data Analytics Professionals.


Syllabus

An overview of the Python language
  • Launching IPython Shell and Jupyter Notebook
  • Keyboard Shortcuts in the IPython Shell
  • Special Commands of Python
    • Pasting Code Blocks: %paste and %cpaste
    • Running External Code: %run
    • Timing Code Execution: %timeit
    • %magic and %Ismagic
  • IPython’s In and Out Objects
  • IPython and Shell Commands
  • Errors and Debugging
  • Profiling and Timing Code
  • Understand Data Types in Python
  • NumPy Arrays
  • Computation on NumPy Arrays: Universal Functions
  • Aggregations: Min, Max and more
  • Computation on Arrays: Broadcasting
  • Comparison, Boolean Logic, and Masks
  • Fancy Indexing
  • Sorting Arrays
  • NumPy’s Structured Array
  • Installing and Using Pandas
  • Pandas Objects
  • Data Indexing and Selection
  • Operating on Data in Pandas
  • Handling Missing Data
  • Hierarchical Indexing
  • Concat and Append
  • Merge and Join
  • Aggregations and Grouping
  • Pivot Tables
  • Vectorised String Operations
  • Working with Time Series
  • eval() and query()
  • Overview of Matplotlibs
  • Two Interfaces
  • Simple Line Plots and Scatter Plots
  • Visualising Errors
  • Density and Contour Plots
  • Histograms, Binnings, and Density
  • Customising Plot Legends
  • Customising Colorbars
  • Multiple Subplots
  • Text Annotation
  • Customising Ticks
  • Customising Matplotlib: Configuration and Stylesheets
  • Three-Dimensional Plotting in Matplotlib
  • Geographic Data with Basemap
  • Visualisation with Seaborn

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