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About Hr Analytics with Python Course

Duration/Mode: 32 Hours Live Online Training.
E-Learning Access: Includes Recorded Videos, Projects, and Case Studies resume & placement support Job Opportunities and
Internship: Get access to job opportunities to top MNCs.
Job Opportunities and Internship: Get access to job opportunities to top MNCs.
Live Projects: Experience Industry oriented Projects during the training.
Prime Membership: Get a 1-Year Prime Membership of Koed and avail the 360o placement support.
Trainer: Industry expert trainers with decades of working and training experience.

Benefits of Koed Hr Analytics with Python Course

1-Yr Prime Membership of Koed and avail the 360o placement support.
100% Job Support exclusively entitled for BI Specialist Professionals.
32-Hours Live Virtual Training.
Access to the Koed LMS.
Recorded Videos of the Session for recap.
Resume analysis and Interview Preparation.

Own a certificate in exchange for your merit and hard work, not just money.

Acquire
In your hand, will rest a Koed certificate once you test your knowledge in the exam and pass with flying colours. Once all modules of the course are done, our trainers will be greatly pleased to share your reward with you.
Think out of the box, go out of your comfort zone
Everybody has an option to do things the conventional way but only some have the courage to own up and do it in their own way. Once you complete all modules of the course, you will be instilled with all the required skills and knowledge to be able to carve your own way. You will see your productivity shoot up at work, during interviews and more.
Share and inspire others
No success story is a great story until it motivates other people to work harder. We would encourage you to highlight and show the world the reward you earned for your hardwork and consistency. Share the certificate on social media channels, alumni networking meetings and other platforms. Remember when it's your time to shine, shine brightly.

Hr Analytics with Python Course Curriculum

Module 1- Analytics - General

This module will guide the candidate with the knowledge of Analytics. Learn about the different roles in Analytics. Know about the tools and techniques in Analytics. Gain knowledge about Data Science, Data Mining, Statistics, machine learning, and more. Learn about the CRISP Modeling Framework.

1.1 What is Analytics (BI, BA, Levels, etc)
1.2 Why Analytics (Appl in various domains
1.3 Different Roles in Analytics
1.4 Tools and Techniques in Analytics
1.5 Data Science, Data Mining, Statistics, Machine Learning, Su
1.6 CRISP Modeling Framework
1.7 Scales of Measurements

Module 2 - Python Environment

This module will help the candidate to gain knowledge about the Python Environment. Learn about Anaconda setup and various IDEs, GIT, and more. Create and Manage Analytics/ML Projects

2.1 Anaconda - Download & Setup
2.2 IDEs - Jupyter, Spyder, PyCharm
2.3 Git - Setup and Configuration with IDEs
2.4 Creating and Managing Analytics/ ML Projects

Module 3 - Basic Programming and Data Structures

This module will help the candidate with knowledge of basic programming and data structures. Gain extensive knowledge about Libraries, NumPy, pandas, Matplotib

3.1 Basic Data Structures & Programming Constructs
3.2 Libraries
3.3 Numpy
3.4 Pandas
3.5 Matplotlib

Module 4 - Pre process and Descriptive Summary

This module will guide the candidate with the knowledge of Data Processing, Data Manipulation, and Descriptive summary. Know about Group summaries, crosstab, pivot, reshape data and manage missing values. Learn to manage indexes in Pandas, Scaling of data, and more

4.1 Pre Processing Data
4.2 Group Summaries
4.3 Crosstab, Pivot and Reshape data
4.4 Managing Missing Values
4.5 Outliers Detection
4.6 Various types of Joins, merge
4.7 Managing indexes in pandas
4.8 Partitioning data into train and test set
4.9 Scaling of Data (useful for Clustering)

Module 5 - Statistics

This module will guide the candidate through the basics of statistics in Business Analytics. Learn extensively about Hypothesis testing, Probability distribution, and Sampling Techniques

5.1 Basic Statistics (mean, median, mode)
5.2 Other Statistics (sd, var, quantile, skewness, kurtosis)
5.3 Hypothesis Tests (t-test, Chi-sq tests, etc)
5.4 Probability Distributions (normal, binomial, etc)
5.5 Sampling Techniques

Module 6 - Graphical Representation of Data

This module will guide you through the techniques of Graphical Representation of Data. Learn about the selection of graphs and types of graphs. Manage plot parameters and advanced graphs such as correlations, heatmap, mosaic, and more

6.1 Selection of Graph
6.2 Basic Graphs (histogram, barplot, boxplot, pie, etc)
6.3 Libraries (matplotlib, seaborn, plotline)
6.4 Managing plot parameters(size, title, axis, legend, etc)
6.5 Advanced Graphs (correlation, heatmap, mosaic, etc)
6.6 Exporting graphs

Module 7 - Modeling Techniques & Linear Regression

This module will guide you through the basic understanding of modeling techniques and Linear Regression. Know about multiple linear regression and its libraries. Learn the metrics of Linear Regressions and its application & assumptions

7.1 Modeling Techniques
7.2 Simple Linear Regression
7.3 Multiple Linear Regression
7.4 Libraries - sklearn, statsmodel
7.5 Predict DV on IVs
7.6 Metrics of Linear Regression(R2, RMSE, p-values)
7.7 Applications of Linear Regression
7.8 Assumptions of Linear Regression

Module 8 - Logistic Regression

This module will guide the learner with knowledge of Logistic Regression. Know the metrics of logistic regression. Predict the probability of DV on IV. Know extensively about applications of Logistic regression

8.1 Difference between Linear and Logistic
8.2 Logistic Regression
8.3 Metrics of Logistic Regression (confusion matrix, ROC curve
8.4 Predict the probability of DV on IV
8.5 Applications of Logistic Regression

Module 9 - Classification

This module will guide the candidate with the knowledge of classification in Financial Analytics. Understand the tree from the plot and know about the classification tree. Learn to improve tree accuracy using random forests. Know the applications of decision tree, KNN, Neural Networks, SVM, and more

9.1 Difference between classification and regression decision t
9.2 Understanding tree from the plot
9.3 Classification Tree - predict class, plot, accuracy
9.4 Regression Tree - predict numerical value, plot, RMSE
9.5 Improving tree accuracy using Random Forests
9.6 Bagging and Boosting
9.7 Applications of Decision Tree
9.8 KNN (k-nearest neighbors)
9.9 Neural Networks
9.10 Gradient Descent
9.11 SVM (Support Vector Machine)

Module 10 - Cluster Analysis

This module will guide you through the knowledge of Cluster Analysis. Know about the Clustering for grouping data and its types. Learn about extracting data in clusters and application of clustering

10.1 Clustering for Grouping Data
10.2 Types - Hierarchical & Non-Hierarchical
10.3 K Means - output metrics (iter, error, plot)
10.4 Hierarchical (Agglomerative & Divisive) - Dendrogram, Visu
10.5 Extracting the data in clusters, Cluster Centers
10.6 Applications of Clustering

Module 11 - Association Rule Analysis

This module will guide you through the knowledge of the Association Rule analysis. Learn to apply AR to the grocery store for market basket analysis. Know about the frequent Itemsets and rules and application of AR

11.1 Applying AR to the grocery store for Market Basket Analysi
11.2 Metrics- Support, Confidence, Lift
11.3 Frequent Itemsets and Rules; Filtering rules
11.4 Applications of AR
12.1 Managing Unstructured Data; Unstructured to Structured Dat
12.2 Extracting Tweets from Twitter
12.3 Extracting words for Sentiment Analysis
12.4 Wordcloud to visualize the frequency of occurrence of word
12.5 Applications of Text Mining

Module 12 - Text Mining

This module will guide the candidate through the understanding of Text Mining. Manage unstructured data and extract tweets from Twitter and words for sentiment analysis. Know the application of text mining