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## T Distribution , Kolmogrov Smirnov, Shapiro Wilk Tests

In a previous tutorial we looked at key concepts in statistical inference. We’ll now look at T Distribution , Kolmogrov Smirnov, Shapiro Wilk, and standard parametric tests. Parametric tests are tests that make assumptions about the parameters of the population distribution from which a sample is drawn. We’ll begin with normality assessment using the Quantile-Quantile Plot (also called the Q-Q plot), the Shapiro-Wilk test and the Kolmogrov Smirnov test. Then, we’ll cover T distribution briefly. Finally, the one sample t-test, which is a standard parametric test will be looked in detail.

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## What is Statistical Inference – Key concepts

In this session, we’ll learn the concept of Statistical Inference.  Statistical inference is a vast area which includes many statistical methods from analyzing data to drawing inferences or conclusions in research or business problems. It plays a vital role in the application of data science across industries.

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## Joins in Python- Merging, Appending and Aggregating Data

In this tutorial, we’ll look at different types of joins in Python used to merge two datasets.Then we’ll study how to aggregate data using the groupby function.

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## Pandas Series and Pandas Dataframe – A Quick Tutorial

In this tutorial we’re going to look at  pandas data structures are and how to use them. We ‘ll start with an introduction to the pandas package and look at  why it is important. We will begin to understand what data structures provides – particularly Pandas Series and Pandas DataFrame – and illustrate how to perform basic tasks on these data structures.

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## Box Plots examples and Advanced Charts

In our other Data Visualization tutorial we looked at commonly used and informative bar and pie charts and how to create them with Python functions. In this tutorial we’ll cover box plot examples and other graph types that we use in data analytics and data science.

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## Data Visualization in Python – Bar Charts and Pie Charts

Data visualization skills are a key part of a of data analytics and data science and in this tutorial we’ll cover all the commonly used graphs using Python. We’ll start with a quick introduction to data visualization in Python and then look at python functions for a range of bars and charts.

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## Scatter Plots, Heat Maps and Bubble Charts in Python

In the third of this series on data visitation tutorials, see part 1 and part 2, we’ll learn how to visually represent relationships between two or more variables and how to create those data visualisations in Python. Specifically, we’ll illustrate how to summarise data using scatterplots, heat maps bubble charts, trend lines and motion charts in Python. You can download the data sets for the practice exercises here.

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## If Else in R – A Tutorial

In this tutorial we’ll look at if..else in R conditional statements

We will start with introduction to conditional statements and then cover different versions of an if statement in R.  The different versions are: the simple if statement, if-else statement, if else function and nested if-else statement. The concepts are similar to R programming.

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## Read CSV in R – Importing data – A Tutorial

In this tutorial we’ll learn about read csv in R, importing and exporting data in R. We’ll start with importing csv and txt files in R using base R functions and then move on to different packages that are used for data management tasks. We’ll also learn how to handle missing observations. Finally we’ll take a brief look at how to import and export data from other software such as SAS, STATA and SPSS.

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## Loops in R and alternatives – A Quick Tutorial

In this tutorial we’ll learn how to write Loops in R.  Like if-else statements, loops are commonly used in data science related programming. We’ll start with an introduction to loops and then cover the various types of loops – the for loop, the while loop and the repeat loop.

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