Data Science with Python in 2020

As you will progress in the Data Science with Python training program, you will get to know the below things

• Statistics for data science
• Basic data cleaning techniques for model building
• Converting your raw data into a machine consumable format
• Working principle of machine learning models and their applicability
• Understanding the parameters required for checking model accuracy
• Deploying the model to make it available as a service
• Maintaining the model over a period of time

• The demand and the supply gap for a data scientist are ever-increasing. In fact, in one of its surveys, IBM predicts increment in data science jobs to be 364,000 to 2720,000 in 2020 which is only going upwards in the subsequent years. Python, as a programming language, is immensely popular for building data science-based applications owing to its simplicity, and large community support and ease of deployment.

  IgmGuru's Data Science with Python online course has been designed keeping in mind about learners who have zero to some level of exposure to Python. Any ideal session in this course would dedicate a good amount of time to understanding the theoretical part after which we will be moving on to the application of theoretical concepts by doing hands-on these statistical techniques. You would be provided with a lot of data set to practice things during the session and also to practice later on in the form of self-study which will help you in your journey of applied data science with python.

  The three main pillars of applied data science with python

  1. Application of mathematical and statistical concepts
  2. Expressing them using a programming language or a tool/platform
  3. Particular business domain

  The Python certification for Data Science modules focuses on explaining various use cases, some of the very famous applications/services which use Python, and then we gradually move to understand data science workflow using Python theoretically. We will help you understand the basic components of any data science model, right from fetching your data from your database to building a model that is in a deployable form.

  What are the key deliverables

  As you will progress in the Data Science with Python online training program, you will get to know the below things

  • Statistics for data science
  • Basic data cleaning techniques for model building
  • Converting your raw data into a machine consumable format
  • Working principle of machine learning models and their applicability
  • Understanding the parameters required for checking model accuracy
  • Deploying the model to make it available as a service
  • Maintaining the model over a period of time

  With respect to the above steps, you will also learn how to use data science specific libraries in Python eg. Frequently used libraries in data cleaning like NumPy, pandas, spicy, groupby, merge; data plotting libraries like matplotlib, seaborn; machine learning-based modules available inside scikit learn for building various regression and classification based algorithms, libraries to check model accuracy like confusion matrix, MSE, RMSE, Natural Language-based libraries like NLTK, genism, VADER. These will help learners with applied data science with python

  A good amount of content has also been dedicated to Natural Language Processing techniques and various web scraping methodologies. Of late, NLP is gaining a lot of popularity owing to use in our day to day life eg. Mails, tweets, FB posts, WhatsApp chats are ideal input for any NLP based models. You are very likely to experience NLP based openings which are nowadays considered to be a specialty within the Machine Learning branch. These are all instances of applied data science with python

  Hence assessing the market-based demands, we have specifically designed modules to upskill you in this area as well – mostly its applied data science with python A very significant model in the area of NLP is Sentiment Analysis which is something we will be building to start things off and will move on to build much complex algorithm in this area.

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