Name: Introduction To HR Analytics with Python Course- Henry Harvin® in Seattle | Henry Harvin
Uploaded: 2023-02-15 10:22:29
Description: Most in-demand Certification globally | Acquire knowledge and skills required to transform HR requirements in support of data-driven decisions | Learn to collect, analyze, and report data for HR management | More than 70% of leaders are gaining ground to incorporate information into regular dynamic cycles I Gain an explicit understanding of basic and advanced concepts of Python programming

Module 1: Introduction To Analytics
- What is Analytics (BI, BA, Levels etc.)
- Why Analytics (Application in various domains..)
- Different Roles in Analytics
- Tools and Techniques in Analytics
- Data Science, Data Mining, Statistics, Machine Learning, Supervised and Non-Supervised Techniques
- Scales of Measurement
- CRISP Modeling Framework
- Quiz Module 1
Module 2: Python Environment
- Anaconda - Download & Setup
- IDEs - Jupyter, Spyder, PyCharm
- Git - Setup and Configuration with IDEs
- Creating and Managing Analytics/ ML Projects
- Quiz Module 2
Module 3: Basic Programming And Data Structures
- Basic Data Structures & Programming Constructs
- Libraries
- Numpy
- Pandas
- Matplotlib
- Quiz Module 3
Module 4: Data Manipulation And Descriptive Summary
- Group Summaries
- Crosstab, Pivot and Reshape data
- Managing Missing Values
- Outliers Detection
- Various types of Joins, merge
- Managing indexes in pandas
- Partitioning data into train and test set
- Scaling of Data (useful for Clustering)
- Quiz Module 4
Module 5: Statistics
- Basic Statistics (mean, median, mode)
- Other Statistics (sd, var, quantile, skewness, kurtosis)
- Hypothesis Tests (t-test, Chi-square tests etc.)
- Probability Distributions (normal, binomial etc.)
- Sampling Techniques
- Quiz Module 5
Module 6: Graphical Representation Of Data
- Selection of Graph
- Libraries (matplotlib, seaborn, plotnine)
- Basic Graphs (histogram, barplot, boxplot, pie etc.)
- Managing plot parameters(size, title, axis, legend etc.)
- Advanced Graphs (correlation, heatmap, mosaic etc.)
- Exporting graphs
- Quiz Module 6
Module 7: Linear & Multiple Regression Analysis
- Simple Linear Regression
- Multiple Linear Regression
- Libraries - sklearn, statsmodel
- Predict DV on IVs
- Metrics of Linear Regression(R2, RMSE, p-values)
- Applications of Linear Regression
- Assumptions of Linear Regression
- Quiz Module 7
Module 8: Logistic Regression
- Difference between Linear and Logistic
- Logistic Regression
- Metrics of Logistic Regression (confusion matrix, ROC curve)
- Predict probability of DV on IV
- Applications of Logistic Regression
- Quiz Module 8
Module 9: Classification: Decision Trees
- Difference between classification and regression decision trees from CART models
- Understanding tree from the plot
- Classification Tree - predict class, plot, accuracy
- Regression Tree - predict numerical value, plot, RMSE
- Improving tree accuracy using Random Forests
- Bagging and Boosting
- Applications of Decision Tree
- KNN (K-nearest neigbours)
- Neural Networks
- Gradient Descent
- SVM (Support Vector Machine)
- Quiz Module 9
Module 10: Cluster Analysis
- Clustering for Grouping Data Types - Hierarchical & Non-Hierarchical
- Kmeans - output metrics (iter, error, plot)
- Hierarchical (Agglomerative & Divisive) - Dendrogram, Visual plot
- Extracting the data in clusters, Cluster Centers
- Applications of Clustering
- Quiz Module 10
Module 11: Association Rule Analysis
- Applying AR to grocery store for Market Basket Analysis
- Metrics- Support, Confidence, Lift
- Frequent Items Sets and Rules; Filtering rules
- Applications of AR
- Quiz Module 11
Module 12: Text Mining
- Managing Unstructured Data; Unstructured to Structured Data
- Extracting Tweets from Twitter
- Extracting words for Sentiment Analysis
- Wordcloud to Visualize the frequency of occurrence of words in text
- Applications of Text Mining
- Quiz Module 12
Module 13: Forecasting With Time Series Analysis
- Creating & Managing dates and times
- Creating and managing time series data
- Download stock prices data using libraries and analyzing them
- Time Series Components(Seasonal, Irregular, Trend) & Types (Additive & Multiplicative)
- Time Series Analysis - Simple Moving Average, Exponential Smoothening, ARIMA forecasting
- Plotting Time Series, Candlesticks diagram for stocks
- Applications of Time Series Analysis
- Quiz Module 13

HR Analytics with Python Course in Seattle

25 Nov 2024

32 Hours

25 Nov 2024

32 Hours

Book a Live Class, For Free!

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Know the complete offerings of our HR Analytics with Python in Seattle

Key Highlights

32 Hours of Instructor-Led Sessions

11 Hours of Live Interactive Doubt Solving Sessions

6 Hours of Live Master Sessions by Industry Experts

192 Hours of Self-Paced Learning

192 Hours Hands on with Cloud Labs

Job-Ready Portfolio of 3 Capstone Projects

82 Auto-Graded Assessments

6+ Industry Case Studies

48 Guided Hands-On Exercises

3+ Assignments and Mini Projects

1 Mock Interview and 1 Hackathon

Get 1-Year Gold Membership of Henry Harvin® Data Science & Analytics Academy

Get a guaranteed Internship with Henry Harvin® and in top MNCs like J.P. Morgan, Accenture & many more via Forage

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2 Hours of Mentorship by Industry Experts

Earn Certification of Course from Henry Harvin®, Govt of India recognized & Award-Winning Institute and NSDC Certification

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You Get 10-In-1 Program

About HR Analytics with Python in Seattle

About The HR Analytics with Python

Who Should Attend This Course?

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Curriculum For HR Analytics with Python in Seattle

Module 1: Introduction To Analytics

Module 2: Python Environment

Module 3: Basic Programming And Data Structures

Module 4: Data Manipulation And Descriptive Summary

Module 5: Statistics

Module 6: Graphical Representation Of Data

Module 7: Linear & Multiple Regression Analysis

Module 8: Logistic Regression

Module 9: Classification: Decision Trees

Module 10: Cluster Analysis

Module 11: Association Rule Analysis

Module 12: Text Mining

Module 13: Forecasting With Time Series Analysis

Admission Closes On 25 Nov 2024

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What you'll Learn in this course

Python Environment

Python Programming Fundamentals

Overview of Python in HR Analytics

Data Cleaning and Preprocessing Techniques

Statistics and Logistic Regression

Transforming Unstructured Data into Structured Data

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