
In this module, there are 2 attachments
Full resource code for the course (all data sheets, r codes and python codes.)
Anatomy of Statistical Modelling - it is a step by step approach towards building an analytical project as it reflects what is to be done next in the journey. This tool will always help in your journey and bring you out from any confusion ever. It is designed to how a project is done in the analytics and statistical areas, as to what comes after what and which technique needs to be used.
It is a step by step approach towards building analytical projects as it reflects what is to be done next in the journey.
This tool will always help you in the process and bring you out of any confusion ever.
It is designed to know how a project is done in the analytics and statistical areas, as to what comes after what and which technique needs to be used. How to clean data, prepare data, and model data.
Learn how to install Python via the Anaconda distribution, choose the right 64-bit or 32-bit installer, set up Anaconda Navigator, and launch Jupyter Notebook for data science work.
Import an Excel file in Python and R by locating the LR data file, copying its path, and using pandas read_excel in Python and the R read function with backslashes.
Load an Excel file in python with pandas as pd, view data via df.head, and inspect dtypes to distinguish int64, object, and float, noting data nature and identifiers.
Learn how to read Excel data in R using a library, inspect data with head and str, and understand numeric and character types to guide statistics, plots, and informed decisions.
Validate for correctness by verifying data imported into Python matches Excel counts, check df.shape for 1470 rows and 17 columns, inspect df.columns, and preview data with head and tail.
Learn to compute mean, median, and mode from an Excel dataset in Python and R, using pandas and read_excel, plus table for mode in R.
Explore salary data analysis by calculating mean, median, min, and max, then assess variability with range, interquartile range, variation, and standard deviation to reveal data dispersion.
Explore the interquartile range, the middle 50% between Q1 and Q3, and its role in data spread. Use the five-number summary (min, Q1, median, Q3, max) to compute IQR.
Learn to calculate standard deviation across Excel, Python, and R using stdev, std, and sd on salary data for 2018, highlighting cross-language equivalents.
Explore skewness in data, an asymmetry from the normal distribution, using Excel examples to distinguish right-skewed, left-skewed, and normal distributions by comparing mean, median, and mode.
Learn to standardize salary data using Python with sklearn's pre-processing scale, and replicate with R's robust package to obtain standardized values relative to the mean.
Explore creating bar charts and pie charts in Excel for categorical data, converting counts to percentages, adding data labels, and understanding differences from histograms.
Explore creating bar and pie charts in Python and R using dataframes, value counts, and proportions, with hands-on steps for plotting and interpreting department distributions.
Detect and impute missing values in Excel, identify missingness by column, quantify percentages, and impute categorical with mode and continuous with mean or median, then replicate in Python and R.
Analyze missing values in R by loading data with readexcel and computing sums across columns. Impute missing values with zero, mean, median, and mode for numeric and categorical variables.
Master box plots as a univariate distribution tool using Q1, Q3, and median to identify outliers with whiskers and the IQR, and apply them to compare continuous and categorical data.
Build box plots in Python and R to detect outliers in HR salary data, filtering HR records and analyzing salary 2018 for extreme values.
Detect outliers by z-score standardization, converting salary data to z units (mean zero, std one), and flagging values beyond three; HR shows none, sales shows six, alongside box plot checks.
Identify outliers using percentile-based capping and flooring to trim extreme values at the 90th percentile, then apply Python and R methods to clip data and visualize with a box plot.
HR analytics is also known as people analytics or you can say talent analytics. It is kind of analytics which helps HR managers, executives to make data-driven decisions about their employee or the workforce. It gives you expertise in using statistics, technology on unused but very important people’s data which can help you in making better business decisions and management for your company.
In this course, we take you on a journey where you start from a simple topic of calculating mean and move on to many complex topics such as text analytics. Hence you kickstart from statistics and land on machine learning techniques.
Once you have completed the course, you can help your company to better drive the ROI. Classic approaches are not sufficient in getting the required result in the long run.
To overcome this gap we came up with a solution where you can learn the techniques of solving these problems on your own in a very simple and intuitive self-paced learning method.
We have tried to create a very simple structure for this course so even if you have no knowledge or very basic knowledge of analytics then even you won't face any problem throughout the course. In this course you will:
Learn applied statistics right from scratch.
Simultaneously learn analytics on R and Python.
Identify the dependent and independent variables in your dataset.
Understand the steps involved in data preparation.
Various methods to measure Central Tendency, Variability, and Shape of data.
Understand the steps involved in Hypothesis Testing, Univariate, and Bi-variate Analysis.
Learn the concepts of Feature Engineering.
Understand the concepts of Statistical model building.
Identify a business problem and its importance.
Understand the concept of Machine Learning – Supervised and Unsupervised Learning Techniques.
4 Hands-on case studies.
More than 20 types of charts/plots.
And the most important is applying machine learning on HR Data and predicting futuristic insights.