Master in Data Science and Data Analytics
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Want to become an Successful Data Scientist but don’t know what to do and how?
Take a look at this course where you will
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Not only learn responsibilities, qualifications, all the knowledge and tools required in detail to become a Successful Data Scientist but also
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The roadmap for becoming a Successful Data Scientist
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Clear your doubts on this topic any time while doing the course
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My exposure to Data Science started 40 years back in 1979-81 at Indian Institute of Management Bangalore when I learnt about various methodologies and tools like Multivariate and Conjoint Analysis to make better management decisions. Data Science was not yet born as it has developed and matured now with developments in Machine Learning and other Algorithms that have made it possible to use those methodologies and tools
While I continued to use the Data Analytics during next about 30 years while working in Unilever, Johnson and Johnson and Danone, I came to know about the fully developed Data Science in 2016 when I started working at IIM Udaipur teaching and coaching MBA students
During past 8 years, I have researched and learnt a lot more about the capabilities of Data Science to help all of us make better decisions
I bring in this course my learnings from this journey and share with you how can you also become an Successful Data Scientist and join this attractive and growing field
Look at what other students saying about this course
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Preview for yourself many lectures free. If you like the content, enroll for the course, enjoy and skill yourself to Become a Successful Data Scientist! If don’t like the content, please message about how can we modify it to meet your expectations.
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2
Overview IAt the end of this lecture, you will learn the following
•What are the responsibilities of a Data Scientist?
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3
Overview IPlease answer following questions based on learnings in this lecture
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4
Overview II
At the end of this lecture, you will learn the following
•What qualifications are required to become a Data Scientist?
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5
Overview II
Please answer following questions based on learnings in this lecture
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6
Overview III
At the end of this lecture, you will learn the following
•How can you become a successful Data Scientist?
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7
Overview III
Please answer following questions based on learnings in this lecture
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8
Data Analysis and Exploration I
At the end of this lecture, you will learn the following
•Conduct exploratory data analysis (EDA) to understand patterns, trends, and anomalies in the data.
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9
Data Analysis and Exploration I
Please answer following questions based on learnings in this lecture
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10
Data Analysis and Exploration II
At the end of this lecture, you will learn the following
•Clean and preprocess data to ensure accuracy and completeness
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11
Data Analysis and Exploration II
Please answer following questions based on learnings in this lecture
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12
Model Development I
At the end of this lecture, you will learn the following
Design, develop, and implement machine learning models to solve business problems
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13
Model Development I
Please answer following questions based on learnings in this lecture
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14
Model Development II
At the end of this lecture, you will learn the following
Design, develop, and implement machine learning models to solve business problems
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15
Model Development II
Please answer following questions based on learnings in this lecture
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16
Model Development III
At the end of this lecture, you will learn the following
Utilize statistical modeling techniques for predictive and prescriptive analytics
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Model Development III
Please answer following questions based on learnings in this lecture
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Model Development IV
At the end of this lecture, you will learn the following
•Utilize statistical modeling techniques for prescriptive analytics.
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Model Development IV
Please answer following questions based on the learnings in this lecture
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20
Model Development V
At the end of this lecture, you will learn the following
•Evaluate and select appropriate algorithms for specific tasks
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Model Development V
Please answer following questions based on learnings in this lecture
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22
Model Development VI
At the end of this lecture, you will learn the following
•Evaluate and select appropriate algorithms for specific tasks
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Model Development VI
Please answer following questions based on learnings in this lecture
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24
Feature Engineering I
At the end of this lecture, you will learn the following
Identify relevant features and variables for model training and optimization
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25
Feature Engineering I
Please answer following questions based on learnings in this lecture
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26
Feature Engineering II
At the end of this lecture, you will learn the following
•Identify relevant features and variables for model training and optimization
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27
Feature Engineering II
Please answer following questions based on learnings in this lecture
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28
Feature Engineering III
At the end of this lecture, you will learn the following
•Collaborate with domain experts to incorporate industry-specific knowledge into analyses
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29
Feature Engineering III
Please answer following questions based on learnings in this lecture
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30
Data Visualization I
At the end of this lecture, you will learn the following
•How to create compelling data visualizations to communicate complex findings to non-technical stakeholders.
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31
Data Visualization I
Please answer following questions based on learnings in this lecture
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32
Data Visualization II
At the end of this lecture, you will learn the following
•How to create compelling data visualizations to communicate complex findings to non-technical stakeholders
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33
Data Visualization II
Please answer following questions based on learnings in this lecture
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34
Data Visualization III
At the end of this lecture, you will learn the following
•How to use tools such as Matplotlib, Seaborn, or Tableau to present insights effectively
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35
Data Visualization III
Please answer following questions based on learnings in this lecture
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36
Collaboration I
At the end of this lecture, you will learn the following
•How to work closely with cross-functional teams, including business analysts, engineers, and decision-makers, to understand business needs and provide data-driven solutions
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37
Collaboration I
Please answer following questions based on learnings in this lecture
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38
Collaboration II
At the end of this lecture, you will learn the following
•How to work closely with cross-functional teams, including business analysts, engineers, and decision-makers, to understand business needs and provide data-driven solutions
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39
Collaboration II
Please answer following questions based on learnings in this lecture
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40
Collaboration III
At the end of this lecture, you will learn the following
•How to work closely with cross-functional teams, including business analysts, engineers, and decision-makers, to understand business needs and provide data-driven solutions
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41
Collaboration III
Please answer following questions based on learnings in this lecture
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42
Experimentation I
At the end of this lecture, you will learn the following
How to design and execute experiments to test hypotheses and measure the impact of various interventions
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43
Experimentation I
Please answer following questions based on learnings in this lecture
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44
Experimentation II
At the end of this lecture, you will learn the following
•How to design and execute experiments to test hypotheses and measure the impact of various interventions.
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45
Experimentation II
Please answer following questions based on learnings in this lecture
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46
Experimentation III
At the end of this lecture, you will learn the following
•How to design and execute experiments to test hypotheses and measure the impact of various interventions.
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47
Experimentation III
Please answer following questions based on learnings in this lecture
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48
Experimentation IV
At the end of this lecture, you will learn the following
How to design and execute experiments to test hypotheses and measure the impact of various interventions
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49
Experimentation IV
Please answer following questions based on learnings in this lecture
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50
Experimentation V
At the end of this lecture, you will learn the following
How to design and execute experiments to test hypotheses and measure the impact of various interventions
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51
Experimentation V
Please answer following questions based on learnings in this lecture
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52
Experimentation VI
At the end of this lecture, you will learn the following
•How to iterate on models and analyses based on feedback and changing business requirements
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53
Experimentation VI
Please answer following questions based on learnings in this lecture
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54
Experimentation VII
At the end of this lecture, you will learn the following
•How to iterate on models and analyses based on feedback and changing business requirements
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55
Experimentation VII
Please answer following questions based on learnings in this lecture
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56
Continuous Learning I
At the end of this lecture, you will learn the following
•Stay abreast of the latest developments in data science, machine learning, and related fields
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57
Continuous Learning I
Please answer following questions based on learnings in this lecture
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58
Continuous Learning II
At the end of this lecture, you will learn the following
•Stay abreast of the latest developments in data science, machine learning, and related fields
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59
Continuous Learning II
Please answer following questions based on learnings in this lecture
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60
Documentation I
At the end of this lecture, you will learn the following
Maintain clear and concise documentation of methodologies
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Documentation I
Please answer following questions based on learnings in this lecture
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Documentation II
At the end of this lecture, you will learn the following
Maintain clear and concise documentation of data sources
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Documentation II
Please answer following questions based on learnings in this lecture
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Documentation III
At the end of this lecture, you will learn the following
Maintain clear and concise documentation of code
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Documentation III
Please answer following questions based on learnings in this lecture
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Documentation IV
At the end of this lecture, you will learn the following
•Maintain clear and concise documentation of methodologies, data sources, and code
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Documentation IV
Please answer following questions based on learnings in this lecture
