Intellipaat’s Data Scientist training program is specially designed for undergraduates. The course is created and taught by top Data Scientists from top product companies to gain real world exposure.This program focuses on helping you become proficient in using a wide range of Machine Learning algorithms and learning their implementations using Python and statistical libraries to solve complex business problems. Since this course is associated with IBM, you will also receive a certification from IBM after completing the course.
In this course, you will be working on real-time projects that provide industry exposure. After the completion of the course, you will be working on the Capstone project, where you will implement the hard-earned Data Science skills to solve real-world problem statements. You can showcase this project to impress your potential employers. Also, this program includes intensive mock interview sessions and resume creation service from experts.
The learning outcome of this course includes the following topics:
There are no specific requirements for pursuing a Data Science certification course for an undergraduate student.
According to Indeed, the average salary of a Data Scientist in the United States is US$121,650 per annum and, in India, it is ₹856,000 per annum. These salary stats show how Data Science jobs are the highest paying jobs in the industry. Data Scientists are hired for adding value to organizations and increasing revenue by extracting business insights.
The subject matter experts at Intellipaat ensure that students gain all the skills required to become a Data Scientist by working on real-life projects. Also, the students can add these projects in their resume or can use it as their final year project.
Intellipaat provides all its learners with job assistance. They will get exposure to connect to 200+ employers and get a chance for three face-to-face interviews. Therefore, it is worth to enroll in Intellipaat’s Data Science certification course.
1.1 What is Data Science
1.2 What does a Data Scientist do
1.3 Various examples of Data Science in the industries and how Python is deployed for Data Science applications
1.4 Various steps in Data Science process like data wrangling, data exploration, and selecting the model
2.1 Introduction to Python programming language
2.2 Important Python features
2.3 How is Python different from other programming languages
2.4 Python installation
2.5 Anaconda Python distribution for Windows, Linux, and Mac
2.6 How to run a sample Python script
2.7 Python IDE working mechanism
2.8 Running some Python basic commands
2.9 Python variables, data types, and keywords
3.1 Understanding the OOP paradigm like encapsulation, inheritance, polymorphism, and abstraction
3.2 What are access modifiers, instances, class members, classes and objects
3.3 Function parameter and return type functions
3.4 Lambda expressions
4.1 Introduction to mathematical computing in Python
4.2 What are arrays and matrices
4.3 Array indexing
4.4 Array math
4.5 Inspecting a NumPy array
4.6 Numpy array manipulation
5.1 Introduction to SciPy
5.2 Building on top of NumPy
5.3 What are the characteristics of SciPy
5.4 Various sub-packages for SciPy like Signal, Integrate, Fftpack, Cluster, Optimize, Stats, and more
5.5 Bayes Theorem with SciPy
6.1 What is data manipulation
6.2 Using the Pandas library for data manipulation
6.3 NumPy dependency of Pandas library, Series object in pandas
6.4 Dataframe in Pandas
6.5 Loading and handling data with Pandas
6.6 How to merge data objects
6.7 Concatenation and various types of joins on data objects
6.8 Exploring dataset, cleaning dataset, manipulating dataset, and visualizing the dataset
7.1 Introduction to visualization
7.2 Introduction to Matplotlib
7.3 Using Matplotlib for plotting graphs and charts like Scatter, Bar, Pie, Line, Histogram, and more
7.4 Matplotlib API, Subplots, and Pandas built-in data visualization
8.1 Revision of topics in Python (Pandas, Matplotlib, NumPy, Scikit-Learn)
8.2 Introduction to machine learning
8.3 Need for Machine learning
8.4 Types of Machine Learning
8.5 Workflow of Machine Learning
8.6 Use cases in Machine Learning and its various algorithms
8.7 What is supervised learning
8.8 What is unsupervised learning
9.1 What is supervised learning
9.2 What is linear regression
9.3 Step by step calculation of Linear Regression
9.4 Linear regression in Python
9.5 Logistic regression
9.6 What is a classification
9.7 Decision Tree, confusion matrix, random forest, Naïve Bayes classifier, Support Vector Machine, and xgboost
10.1 Introduction to unsupervised learning
10.2 Use cases of unsupervised learning
10.3 What is clustering
10.4 Types of clustering(self-paced)- exclusive clustering, overlapping clustering, and hierarchical clustering(self-paced)
10.5 What is K-means clustering
10.6 Understanding the K- means clustering algorithm
10.7 Step by step calculation of the K-means algorithm
10.8 Demo on K-means using Scikit
10.9 Association rule mining
10.10 Market basket analysis
10.11 Measures in association rule mining -support, confidence, and lift
10.12 Apriori Algorithm
10.13 Demo on Apriori
11.1 Introduction to PySpark
11.2 Who uses PySpark
11.3 Need for Spark with Python
11.4 Basics of PySpark
11.5 Pyspark in industry
11.6 PySpark installation
11.7 PySpark fundamentals
11.8 Advantages over MapReduce
11.9 PySpark use-cases and PySpark demo
Project 1: Analyzing the naming trends using Python
Domain: Medical Industry
Problem Statement: Extract the names of babies born in the year from the hospitals and find the most popular names.
Topics: Python features, Array indexing, Logistic regression
Project 2: Performing Analysis on Customer Churn Dataset
Problem Statement: Predicting if the customer will churn or not in your telecom-based company.
Topics: Data manipulation, Data Visualization, Supervised learning
Project 3: Netflix-Recommendation system
Problem Statement: Analyze the movies on Netflix and use the ratings for movie recommendation.
Topics: K-means clustering, SciPy, PySpark
Project 4: Python Web Scraping for Data Science
Domain: Web Scraping
Problem Statement: Install all web scraping libraries and
Topics: Machine Learning, Matplotlib, Access modifiers
As part of this training, you will be working on real-time projects and assignments that have immense implications in the real-world industry scenarios, thus helping you fast-track your career effortlessly.
Intellipaat Course Completion Certificate will be awarded upon the completion of the project work (after expert review) and upon scoring at least 60% marks in the assignments that will be made available as part of the training program. Intellipaat certification is well recognized in top 80+ MNCs like Ericsson, Cisco, Cognizant, Sony, Mu Sigma, Saint-Gobain, Standard Chartered, TCS, Genpact, Hexaware, etc. for its structured learning, industry-oriented projects, professional teaching methodologies among other advantages over others in the industry.
Since the projects are industry-level and from various domains, alongside learning and mastering the concepts, this can be repurposed to be the final year project as well. It shows that you’ve thoroughly learned the concepts and poses as a huge advantage for potential employers.
At Intellipaat, you can enroll in either the instructor-led online training or self-paced training. Apart from this, Intellipaat also offers corporate training for organizations to upskill their workforce. All trainers at Intellipaat have 12+ years of relevant industry experience, and they have been actively working as consultants in the same domain, which has made them subject matter experts. Go through the sample videos to check the quality of our trainers.
Intellipaat is offering the 24/7 query resolution, and you can raise a ticket with the dedicated support team at anytime. You can avail of the email support for all your queries. If your query does not get resolved through email, we can also arrange one-on-one sessions with our trainers.
You would be glad to know that you can contact Intellipaat support even after the completion of the training. We also do not put a limit on the number of tickets you can raise for query resolution and doubt clearance.
Intellipaat is offering you the most updated, relevant, and high-value real-world projects as part of the training program. This way, you can implement the learning that you have acquired in real-world industry setup. All training comes with multiple projects that thoroughly test your skills, learning, and practical knowledge, making you completely industry-ready.
You will work on highly exciting projects in the domains of high technology, ecommerce, marketing, sales, networking, banking, insurance, etc. After completing the projects successfully, your skills will be equal to 6 months of rigorous industry experience.
Intellipaat actively provides placement assistance to all learners who have successfully completed the training. For this, we are exclusively tied-up with over 80 top MNCs from around the world. This way, you can be placed in outstanding organizations such as Sony, Ericsson, TCS, Mu Sigma, Standard Chartered, Cognizant, and Cisco, among other equally great enterprises. We also help you with the job interview and résumé preparation as well.
You can definitely make the switch from self-paced training to online instructor-led training by simply paying the extra amount. You can join the very next batch, which will be duly notified to you.
Once you complete Intellipaat’s training program, working on real-world projects, quizzes, and assignments and scoring at least 60 percent marks in the qualifying exam, you will be awarded Intellipaat’s course completion certificate. This certificate is very well recognized in Intellipaat-affiliated organizations, including over 80 top MNCs from around the world and some of the Fortune 500companies.
Apparently, no. Our job assistance program is aimed at helping you land in your dream job. It offers a potential opportunity for you to explore various competitive openings in the corporate world and find a well-paid job, matching your profile. The final decision on hiring will always be based on your performance in the interview and the requirements of the recruiter.