Why should you learn statistics now? 

• Jobs in Analytics – Data is everywhere, and with that is the need to have professionals who can interpret that data which requires a solid understanding of statistical concepts

• Demand is only going to go up – With more organizations realizing the potential of statistics for their business, data analysts knowing statistics will excel over the ones just knowing technical tools. For a good Data Analyst, it is THE backbone!

• Build your Career in Data Science, Artificial Intelligence and Data Analysis - Statistics remains the fundamental skill set of those pursuing a career in Data Science, Deep Learning, Artificial Intelligence, Management Consulting, Financial Analysis

• Improve your understanding of the world - Statistics is used in everyday life, to describe the stock market, house prices, economic performance, business and much much more.  Improve your data literacy and equip yourself with the ability to communicate in the language of the future.

Why learn from us?

• Save your time and your money with us by using the exact training courses that have been used to equip over 1000 beginners with statistical skills that they can apply to their day-to-day jobs

• Leverage our real-world understanding and experience, with instruction from real-life analytics experts who have built their careers applying statistics to real business problems on a day-to-day basis

• Develop your skills in Data Analytics & Data Science with our expanding range of high quality programs, that equip you with the real-world skills needed to succeed in the workplace of the future

What is Statistics all about?

Understanding statistics is essential to understand research in the social and behavioral sciences. In this course you will learn the basics of statistics; not just how to calculate them, but also how to evaluate them. This course will also prepare you for the next course in the specialization - the course Inferential Statistics. In the first part of the course we will discuss methods of descriptive statistics. You will learn what cases and variables are and how you can compute measures of central tendency (mean, median and mode) and dispersion (standard deviation and variance). Next, we discuss how to assess relationships between variables, and we introduce the concepts correlation and regression. The second part of the course is concerned with the basics of probability: calculating probabilities, probability distributions and sampling distributions. You need to know about these things in order to understand how inferential statistics work. The third part of the course consists of an introduction to methods of inferential statistics - methods that help us decide whether the patterns we see in our data are strong enough to draw conclusions about the underlying population we are interested in. We will discuss confidence intervals and significance tests. You will not only learn about all these statistical concepts, you will also be trained to calculate and generate these statistics yourself using freely available statistical software.

Statistics encompasses the collection, analysis, and interpretation of data and provides a framework for thinking about data. Statistics is used in many areas of scientific and social research, is critical to business and manufacturing, and provides the mathematical foundation for machine learning and data mining.

In this course, students will gain a comprehensive introduction to the concepts and techniques of statistics as applied to a wide variety of disciplines. 

This course covers basic statistics, such as calculating averages, medians, modes, and standard deviations. With easy-to-understand examples combined with real-world applications from the worlds of business, sports, education, entertainment, and more.  

This course provides you with the skills and knowledge you need to start analyzing data. You'll explore how to use data and apply statistics to real-life problems and situations

What will you learn?

This course is your complete guide to the fundamentals of statistics and probability

1.    Descriptive Statistics

2.    Measures of Central Tendency

3.    Correlation

4.    Linear Regression

5.    Probability Theory

6.    Discrete and Continuous Probability Distributions

7.    Sampling Distributions

8.    Confidence Intervals

9.    Significance Tests.

The first chapter will focus on one variable analysis - we start with discussing the various descriptive statistics, how data can be represented using frequency tables and then move on to discussing measures of central tendency and their interpretation.

The second module will focus on Correlation and Simple Linear Regression and show how these can be calculated in Excel using various formulae. We will then also discuss the interpretation of these results

The next module will focus on introducing the fundamental concepts of Probability Theory like the sample space, events, randomness and basic set theory. We will then move onto discussing conditional probability and introduce the concept of mutually exclusive events.  You will also learn how to calculate probabilities within this section.

In the fourth module, we will discuss Probability Distributions and show the difference between a continuous and discrete distributions, using the Normal and Binomial distributions as key examples.

The next chapter will discuss Sampling Distributions and introduce the Central Limit Theorem and discuss why sampling is important. We will also link the Central Limit Theorem to the normal distribution and explain why this result is so critical to statistical analysis.

The sixth module will focus on confidence intervals and the use of intervals to estimate the true average or proportion of a distribution. We will also discuss the relationship between the significance level (alpha) and the confidence level and teach you how to generate your own confidence intervals in Excel.

The last module will introduce the concept of statistical Hypothesis Testing - what it can tell you and what it can't (i.e. you can't prove anything); how to define the null and alternate hypotheses correctly; getting the direction and 'tails' of your distribution correct and finally Type I and Type II errors with example and interpretations.

This course is the go-to course for building a solid foundation in statistics and probability, not only teaching you the theory but giving you a comprehensive set of real examples you can work on which hasn't been seen in any other course on this platform.

There are helpful quizzes included in all sections that will help you to cement the concepts learnt. Also, we are committed to add new practice activities so that you can practically apply the concepts learnt in this course.