Statistics Essentials: Data Analytics Course
About Data Analytics
Data analytics (DA) is the process of examining data sets in order to draw conclusions about the information they contain, increasingly with the aid of specialized systems and software. Data analytics technologies and techniques are widely used in commercial industries to enable organizations to make more-informed business decisions and by scientists and researchers to verify or disprove scientific models, theories, and hypotheses.
As a term, data analytics predominantly refers to an assortment of applications, from basic business intelligence (BI), reporting, and online analytical processing to various forms of advanced analytics. In that sense, it’s similar in nature to business analytics, another umbrella term for approaches to analyzing data — with the difference that the latter is oriented to business uses, while data analytics has a broader focus. The expansive view of the term isn’t universal, though: In some cases, people use data analytics specifically to mean advanced analytics, treating BI as a separate category.
About Statistics Essentials
If you use a transportation metaphor you might say that statistics and machine learning are the vehicles that take us to both new and familiar places. You should also think of programming and software engineering as the roads and infrastructure necessary to make travel even possible. Linear algebra and basic statistics on the other hand align well with the motors and engines that propel our vehicles.
About this Course
A self-paced data analytics course will help you to understand the various Statistical Techniques from the very basics and how each technique is employed on a real-world data set to analyze and conclude insights. Statistics and its methods are the backend of Data Science to “understand, analyze and predict actual phenomena”. Machine learning employs different techniques and theories drawn from statistical & probabilistic fields.
Why learn Statistics Essentials for Analytics?
Statistics and its methods are the backend of Data Science to understand, analyze, and predict actual phenomena. Machine learning employs different techniques and theories drawn from statistical & probabilistic fields. This Statistics Essentials for Analytics Course enables you to gain knowledge of the essential statistics required for analytics and Data Science, understand the mechanism of popular Machine Learning Algorithms like K-Means Clustering, Regression. The course will also take you through a glimpse of hypothesis testing and its methods enabling you to perform a test on alternative hypotheses.
What you will learn from this course?
- Statistics and its methods, Different techniques and theories from statistical & probabilistic fields
- Knowledge of the essential statistics required for analytics and Data Science
- Mechanism of popular Machine Learning Algorithms like K-Means Clustering, Regression
- Hypothesis testing and its methods
Who should go for this course?
The data analytics course is designed for all those who want to learn essential statistics required for Data Science and Data analytics. The curated statistics course will assist you to form a strong foundation for the Data Science and predictive modeling (nowadays Machine Learning) field.
The following professionals should go for this course:
- Developers aspiring to be a ‘Data Scientist’
- Analytics Managers who are leading a team of analysts
- Business Analysts who want to understand Machine Learning (ML) Techniques
- Information Architects who want to gain expertise in Predictive Analytics
- ‘R’ professionals who want to captivate and analyze Big Data
- Analysts wanting to understand Data Science methodologies
What are the objectives of this Data Analytics Course?
After the completion of this data analytics course, you should be able to learn:
- Analyze different types of data
- Master different sampling techniques
- Illustrate Descriptive statistics
- Apply a probabilistic approach to solve real-life complex problems
- Explain and derive Bayesian inference
- Understand Clustering techniques
- Understand Regression modeling
- Master Hypothesis
- Illustrate Testing the data
1. Understanding the Data
- Introduction to Data Types.
- Numerical parameters to represent data
- Information Gain.
- Statistical parameters to represent data.
2. Probability and its uses
- Uses and need of probability.
- Bayesian Inference, Density Concepts, and Normal Distribution Curve.
3. Statistical Inference
- Point Estimation and Confidence Margin.
- Hypothesis and Levels of Hypothesis Testing.
4. Data Clustering
- Association and Dependence.
- Causation, Correlation, and Covariance.
- Simpson’s Paradox and Clustering Techniques.
5. Testing the Data
- Parametric and Parametric Test Types or Non- Parametric Test.
- Experimental Designing and A/B testing.
6. Regression Modelling
- Logistic and Regression Techniques or Problem of Collinearity.
- WOE and IV Residual Analysis.
- Heteroscedasticity and Homoscedasticity.
You will be able to execute the practicals shown in ‘R’ which is an open-source tool and also stepwise set-up guide for R will be provided to you.
No prerequisites are required for this course.
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Specification: Statistics Essentials for Analytics