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About Course

Data science deals with the analysis of vast volumes of data using modern tools and techniques to find unseen patterns, derive meaningful information, and make business decisions.

Data Science Fundamentals with Python Live is an interactive course that will help you understand analytical techniques with data like exploration, visualization and various predictive analytic techniques by implementing real-life, industry-oriented data science projects with concepts about various popular machine learning algorithms using data science programming language.

All classes are instructor-led and online. Both 1-1 training sessions and groups classes are possible. Enroll to take a free trial.

Skills You Will Gain

Statistical Techniques Business Decisions Analytical Thinking Python Numpy PANDAS Data Analytics Types Data Modelling Data Clustering Hierarchical Clustering

Course Offerings

Instructor-led interactive classes
Clarify your doubts during class
Access recordings of the class
Attend on mobile or tablet
Live projects to practice
Case studies to learn from
Lifetime mentorship support
Industry specific curriculum
Certificate of completion
Employability opportunity

Topics
Instructor (1)
Reviews

1 Statistical techniques that a Data Scientist need Know

50 Statistical techniques

2 Data Analytic Thinking

Outline the importance of data in making business decision
Describe types of data and data catagories
Describe the people and processes involved in data cycle
Compare the characteristics of small data and big data
Discuss the importance of data analytic for business decision making
Identify actions taken during the cross industry standard process of data mining
Discuss the data science links with other discipline

3 Data Analysis with Python

Review and delineate the evolution and purpose of Python
Describe and set up Python development environment
Practice coding with basic Python commands, operators and conditional statements
Explore and apply Python data structure concepts such as array, list, tuple, set and dictionary
Import python modules and packages
Import Python libraries such as NumPy, Pandas

4 Types of Data Analytics

Describe spectrum of business analytics Describe application of descriptive analytics Draw conclusions from a given set of data by using descriptive analytic techniques Describe application of diagnostics analytics Draw conclusions from a given set of data by using diagnostics analytic techniques Describe application of predictive analytics Draw conclusions from a given set of data by using predictive analytic techniques Describe application of prescriptive analytics Draw conclusions from a given set of data by using prescriptive analytic techniques

5 Data Modelling

Discuss Cross Industry Standard Process in Data Modeling Discuss a Generic data modeling process Apply prior knowledge the address the business problems

6 Data Clustering

Describe concepts of clustering and visualize data
Apply K-means algorithm to cluster the data
Apply Z-score method to standardise the data
Interpret the cluster centre and create product segment
Use Dendrogram and Elbow Curve for estimating the number of clusters
Estimate the quality of clustering using Silhouette scores

7 Hierarchical Clustering

Explain the limitations of K-means clustering
Apply hierarchical clustering to the product segmentation and the Gaussian
Distributed dataset
Describe the DBSCAN clustering technique and its benefits
Apply K-Means, Hierarchical and DBSCAN clustering to the moon dataset
Discuss the limitations of clustering algorithms and techniques to address them

Dr. Suresh Kumar

About Instructor

Innovative, Research Oriented, Quality Minded and result oriented Disruptive Data Scientist with 26+ years of experience executing data-driven analytical solutions to increase efficiency, accuracy and utility of internal data processing. Master in Experiential Learning methodology. Experienced at creating predictive data models, BeSpoke ML/DL algorithms for efficient AI and analyzing data mining algorithms to deliver insights and implement action-oriented solutions to complex business problems

4.0

Ratings

12

Reviews

4182

Students

45

Courses

(12)

"I appreciated the course content and the way it was delivered. Despite language barriers, I could follow the lessons. I'd rate it a solid 4-star."

Srivaths Ravva

Dec. 19, 2023, 3 p.m.

"This course provided a decent introduction to data science using Python. The instructor was supportive, which made it easier for me to learn. I'd rate it a 3 out of 5."

Srinivasarao Bellamkonda

Dec. 19, 2023, 2:59 p.m.

"The course was informative and beginner-friendly. Although my English is not strong, I managed to learn a lot. I'd rate it a 4-star."

Sahasra Yeluri

Dec. 19, 2023, 2:59 p.m.

"It was a good experience overall. I could understand most of the content despite my limited English. The instructor's guidance was effective. I'd rate it a 3-star."

Swetha Pandiri

Dec. 19, 2023, 2:58 p.m.

"I found the course to be a great starting point. The instructor's approach helped me grasp the concepts, and the practical examples were beneficial. I'd give it a 4 out of 5."

Akshay Penugonda

Dec. 19, 2023, 2:57 p.m.

"For someone new to data science, this course was just right. The material was well-organized, and I liked the interactive sessions. I'd rate it a 4-star."

Saket Gupta Yakkala

Dec. 19, 2023, 2:57 p.m.

"The course provided a solid foundation in data science basics. Even with limited English skills, I could follow along easily. I'd rate it a 3 out of 5."

Vaasu Guduguntla

Dec. 19, 2023, 2:56 p.m.

"As a beginner, I found the course beneficial. The instructor's pace was good, and the hands-on exercises were practical. I'd rate it a 4 out of 5."

Lakshmi Mattupalli

Dec. 19, 2023, 2:55 p.m.

"The course gave me a good introduction to working with data using Python. The instructor was patient and made complex concepts understandable. I'd give it a 4-star rating."

P Lakshmi P