Data is the fuel of the new era. Since every industry in the world is driven by data, it is integral that every company has enough personnel to approach the data in a multidisciplinary way. Be it statistical, graphical or mathematical, the aim is to analyse and manage data so that companies can take important financial and managerial decisions.

To process raw data and transform into useful set of data, we need an efficient programming language that has the capability to handle large volume of data. That’s where python comes in. Python is well-known for its versatility and features a wide range of advanced functionalities. It has become a key skill that employers consider at the time of short-listing a resume during the selection process of a data scientist position. Python has its own dedicated library for data analysis and its built-in features makes it the preferred option of data scientist all around the world.

Who are eligible to learn Python for Data Science Course?

  • Students having a degree in Engineering/Computer Science.
  • Students with strong coding skills
  • IT Professionals who are interested in the field of analytics.
  • IT professionals who want to upgrade their skills constantly.
  • Anyone having a genuine interest in the field of data science

How is python used in data science?

Data science by definition is the field that uses scientific data, algorithm, processes and systems to extract knowledge and insights from unstructured noisy data. One of the most versatile tools we can use to denoise and structure the above said data is python. Python is the most popular among the programming languages that are used for data analysis.

Python has a lot of integrated libraries for us to use. Libraries are modules of prewritten code which makes it easier for us to do simple tasks. The said libraries allows us to integrate multiple sections of code to make working on them easier. For example, it is a troublesome task to write some kind of code from scratch. Libraries in python like pandas, NumPy, matplotlib makes data cleaning, data analysis, data visualisation easier. The most popular libraries used in python for data science and machine learning applications are

  • NumPy : This is a library used for working with multidimensional arrays, doing mathematical tasks on large, etc
  • NumPy : It is used mainly for data analysis and data cleaning, and it is one of the easiest to use libraries available
  • Keras : It is a high-level neural network API developed for solving machine learning problems. It also acts as the interface for the TensorFlow library
  • TensorFlow : It is an open-source platform for building and training neural networks. TensorFlow can run on almost any platform thanks to its flexible architecture
  • NLTK : Natural language toolkit is a set of libraries in python that aids in natural language processing. It contains text processing libraries for tokenization, parsing, classification, stemming, tagging and semantic reasoning.
  • OpenCV : Open-source computer vision library is used for real-time computer vision. From engineering stand point, computer vision aims to understand and automate the tasks that the human visual system can do.

How do I start python for data science?

The steps for starting python for data science is similar to any other process there is. It’s a step-by-step approach where every step is important.

  • Start the fundamentals of python
    It’s important to start with a strong foundation.
  • Learn regular expressions in python
    Regular expressions will come in handy when dealing with large amount of data, our faculty has prepared numerous techniques so that you can learn all the tips and techniques easily.
  • Learn scientific libraries in python
    As we’ve mentioned earlier about the types of libraries in python, It is important to learn all of these libraries and their uses.
  • Effective data visualisation
    Learn how to properly visualise your desired data.
  • Practice!!
    Even if practice doesn’t make perfect, it does make progress. Try experimenting with projects and take on new challenges to broaden your understanding of the language and the complex world of data science

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Curriculum
  • Course Overview
  • Python Course
  • Python for Data Analysis - Numpy
  • Python for Data Analysis - Pandas
  • Python for Data Visualization - Matplotlib
  • Python for Data Visualization - Seaborn
  • Data Science Capstone Project
  • Introduction to Machine Learning
  • Regression
  • Classification
  • Clustering
  • Machine Learning Capstone Project
  • Advanced ToolS
  • Real World Projects Domains

Data is the fuel for the future of industries. If you want to stay ahead of the race and make a good earning for yourself, then data analysis is the way you should go.

Technically python is the programming language in which data analysis is done. It has a lot many tools and libraries that helps in the process of analysing, visualising, processing and cleaning data. The ability of the program to do all of this while being versatile and cross platform compatible makes it a suitable choice for developers to use it for data analysis application.

The simple example of data analysis is that whenever we take a decision, its dependant on the information we got corresponding to it. Data analysis allows us to extract necessary information from raw data helping us to make responsible decisions.

The 2 main types of data analysis are quantitative and qualitative.
  • Quantitative data analysis deals with numerical data like statistics, percentage, measurements, calculations and more.
  • Qualitative data analysis deals with non-numerical data, typically labels, specific identifiers, and categorical variables.
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