Undoubtedly, Data Science is one of the most revolutionary technologies of our era. It involves
deriving useful insights from data to solve complex real-world problems. This session is a full
course on Data Science that covers everything you need to know to master the field.
Before we get started, let's take a look at the agenda. The first module is an introduction to Data
Science, which covers all the basic fundamentals. Next, we have the Statistics and Probability
module, where you'll understand the math behind Data Science and Machine Learning
algorithms. Then, we have the Basics of Machine Learning module, where you'll learn about
different types of Machine Learning algorithms.
After that, we move on to the Supervised Learning Algorithms module, which covers Linear
Regression, Logistic Regression, Decision Trees, Random Forest, K-Nearest Neighbor, and
Naive Bias. Each of these modules explores how these algorithms can be used to solve
complex problems with the help of real-world examples.
Introduction to Data Science
Data science is currently one of the most in-demand technologies due to the increasing amount
of data being generated and the need to process and make sense of it. In this session, we will
discuss data science in depth, including its various sources, the data life cycle, machine
learning, and more.
Agenda
Sources of data
How Walmart uses data science
What is data science?
Who are data scientists?
Data science job roles
Data life cycle
Basics of machine learning
K-means algorithm and use case
Sources of Data
The evolution of technology and the introduction of IoT and social media have led to an
explosion of data being generated.
Sources of data include:
● Weblogs and web traffic
● Sensor data
● Mobile devices
● Social media
● Cloud applications
deriving useful insights from data to solve complex real-world problems. This session is a full
course on Data Science that covers everything you need to know to master the field.
Before we get started, let's take a look at the agenda. The first module is an introduction to Data
Science, which covers all the basic fundamentals. Next, we have the Statistics and Probability
module, where you'll understand the math behind Data Science and Machine Learning
algorithms. Then, we have the Basics of Machine Learning module, where you'll learn about
different types of Machine Learning algorithms.
After that, we move on to the Supervised Learning Algorithms module, which covers Linear
Regression, Logistic Regression, Decision Trees, Random Forest, K-Nearest Neighbor, and
Naive Bias. Each of these modules explores how these algorithms can be used to solve
complex problems with the help of real-world examples.
Introduction to Data Science
Data science is currently one of the most in-demand technologies due to the increasing amount
of data being generated and the need to process and make sense of it. In this session, we will
discuss data science in depth, including its various sources, the data life cycle, machine
learning, and more.
Agenda
Sources of data
How Walmart uses data science
What is data science?
Who are data scientists?
Data science job roles
Data life cycle
Basics of machine learning
K-means algorithm and use case
Sources of Data
The evolution of technology and the introduction of IoT and social media have led to an
explosion of data being generated.
Sources of data include:
● Weblogs and web traffic
● Sensor data
● Mobile devices
● Social media
● Cloud applications
