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Course Overview

Courses Overview

Data Science is a multi-disciplinary domain that uses lots of methods, processes, algorithms, statistics to collect raw unstructured data, analyze the collected data to derive some meaningful information and insights that can be used for making predictions and decision making. In the closure of Flipkart v. Walmart deal, Data Science was used to help save some losses. Also, data science techniques have taken the Oyo project to unprecedented heights in hospitality sector. These are some of examples on how Data Science has been benefitting our businesses.

Data science is an integration of artificial intelligence. Data science is used to solve complex problems in a simple way. Data science will help test your business. Let us put it in a better way, If a customer buys something from you from his or her past, then based on the customer’s past history and the data we had we can customize the products to him and there is little chance of losing the customer.

The beauty of data science is automotive, Imagine your car taking you to your home without the need for anyone. The vehicle can receive information from the sensors and drive accordingly and result in a reduction in the accident rate. The most important factor in data science is the prediction of natural disasters. We can save many lives by using this technology.

Our Data science course plays a key role in making the right route planning so that customers get the flight at the required time and time. Predictability analysis will be performed using data science to predict flight details. Data science is also used in the supply chain to use the shortest and best way to ship products. With the simplest data science training professionals, Upskill IQ trains one to practice the art of Data Science. The term “Data Scientist” has been coined after considering the very fact that a Data Scientist draws tons of data from the scientific fields and applications whether it’s statistics or mathematics.

About Course

Upskill iQ has designed its Data Science course in a way to equip you with various techniques to collect the correct and interesting data, to be able to analyze any new set of data, and to utilize the findings for business purposes. Whether you are interested in being a Data Scientist, Data Analyst or any other expert related to the field, Upskill IQ has a course for you. With Python being one of the most readable and popular languages, Upskill IQ makes sure you gain your expertise in it while you are gaining expertise in Data Science which will help you train in presenting the data in a much more useful form as compared to the raw data available to them from structured as well as unstructured forms.

Data Science was ranked as bestjob of the year in 2018 and for three consecutive years by Glass Door and it is showing no signs of slowing down. Data Science is a hot career option currently with ever growing jobs in industries across all sectors. India is second only to the United States in generating 50,000 jobs in 2020 in the field of Data Science. With the 21st century information revolution, industries are investing in Data Science to generate meaningful data for critical decision making. Harvard Business School has dubbed Data Science “sexiest job of 21st Century”. For aspiring candidates looking for exponential future growth, Data Science is the domain to get skilled. Bangalore is widely considered the “Silicon Valley of India” (or “IT capital of India”) due to its role and with the rising importance of Data Science, the power to require existing data that’s not necessarily useful on its own and mix it with other data points to get insights on a corporation are often used to learn more about its customers and audience.


  • Write clean, modular, and well-documented code
  • Refactor code for efficiency
  • Create unit tests to test programs
  • Write useful programs in multiple scripts
  • Track actions and results of processes with logging
  • Conduct and receive code reviews
  • Understand when to use object oriented programming
  • Build and use classes
  • Understand magic methods
  • Write programs that include multiple classes, and follow good code structure
  • Learn how large, modular Python packages, such as pandas and scikit-learn, use object oriented programming
  • Portfolio Exercise: Build your own Python package
  • Learn about the components of a web app
  • Build a web application that uses Flask, Plotly, and the Bootstrap framework
  • Portfolio Exercise: Build a data dashboard using a dataset of your choice and deploy it to a web application
  • Understand what ETL pipelines are
  • Access and combine data from CSV, JSON, logs, APIs, and databases
  • Standardize encodings and columns
  • Normalize data and create dummy variables
  • Handle outliers, missing values, and duplicated data
  • Engineer new features by running calculations
  • Build a SQLite database to store cleaned data
  • Prepare text data for analysis with tokenization, lemmatization, and removing stop words
  • Use scikit-learn to transform and vectorize text data
  • Build features with bag of words and tfidf
  • Extract features with tools such as named entity recognition and part of speech tagging
  • Build an NLP model to perform sentiment analysis
  • Understand the advantages of using machine learning pipelines to streamline the data preparation and modelling process
  • Chain data transformations and an estimator with scikit-learn Pipeline
  • Use feature unions to perform steps in parallel and create more complex workflows
  • Understand how to set up an experiment, and the ideas associated with experiments vs. observational studies
  • Defining control and test conditions
  • Choosing control and testing groups
  • How it works and its limitations
  • Sources of Bias: Novelty and Recency Effects
  • Multiple Comparison Techniques (FDR, Bonferroni, Tukey)
  • Portfolio Exercise: Using a technical screener from Starbucks to analyse the results of an experiment and write up your findings
  • Machine Learning Algorithms
  • Popular ML Algorithms, Clustering, Classification and Regression, Supervised vs Unsupervised
  • Choice of ML
  • Supervised Learning
  • Simple and Multiple Linear regression, KNN, and more
  • Linear Regression and Logistic Regression

Course Content


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