Statistics for Data Scientists Training
Statistics for Data Scientists Course:
This statistics course works as a solid foundation for somebody getting started with data science or machine learning. Understanding probability, regression, sampling etc are integral part of this course. Understanding regression, cost function, distance between vectors, hyper-parameter tuning, regularization. Discussion around plotting data, accuracy error calculation.
Statistics for Data Scientists Course Curriculum
Elements of Structured Data
Rectangular Data
Nonrectangular Data Structures
Estimates of Location
Median and Robust Estimates
Further Reading
Standard Deviation and Related Estimates
Example: Variability Estimates of State Population
Exploring the Data Distribution
Frequency Table and Histograms
Further Reading
Mode
Further Reading
Scatterplots
Exploring Two or More Variables
Two Categorical Variables
Visualizing Multiple Variables
Random Sampling and Sample Bias
Random Selection
Sample Mean versus Population Mean
Selection Bias
Further Reading
Central Limit Theorem
Further Reading
Resampling versus Bootstrapping
Confidence Intervals
Normal Distribution
Long-Tailed Distributions
Student’s t-Distribution
Binomial Distribution
Poisson and Related Distributions
Exponential Distribution
Weibull Distribution
Summary
A/B Testing
Why Just A/B? Why Not C, D…?
Hypothesis Tests
Alternative Hypothesis
Further Reading
Permutation Test
Exhaustive and Bootstrap Permutation Test
For Further Reading
P-Value
Type 1 and Type 2 Errors
Further Reading
Further Reading
Further Reading
Further Reading
F-Statistic
Further Reading
Chi-Square Test: A Resampling Approach
Fisher’s Exact Test
Further Reading
Further Reading
Sample Size
Summary
Simple Linear Regression
Fitted Values and Residuals
Prediction versus Explanation (Profiling)
Multiple Linear Regression
Assessing the Model
Model Selection and Stepwise Regression
Prediction Using Regression
Confidence and Prediction Intervals
Dummy Variables Representation
Ordered Factor Variables
Correlated Predictors
Confounding Variables
Testing the Assumptions: Regression Diagnostics
Influential Values
Partial Residual Plots and Nonlinearity
Polynomial
Generalized Additive Models
Naive Bayes
The Naive Solution
Further Reading
Covariance Matrix
A Simple Example
Logistic Regression
Logistic Regression and the GLM
Predicted Values from Logistic Regression
Linear and Logistic Regression: Similarities and Differences
Further Reading
Confusion Matrix
Precision, Recall, and Specificity
AUC
Further Reading
Undersampling
Data Generation
Exploring the Predictions
K-Nearest Neighbors
Distance Metrics
Standardization (Normalization, Z-Scores)
KNN as a Feature Engine
A Simple Example
Measuring Homogeneity or Impurity
Predicting a Continuous Value
Further Reading
Bagging
Variable Importance
Boosting
XGBoost
Hyperparameters and Cross-Validation
Principal Components Analysis
Computing the Principal Components
Further Reading
A Simple Example
Interpreting the Clusters
Hierarchical Clustering
The Dendrogram
Measures of Dissimilarity
Multivariate Normal Distribution
Selecting the Number of Clusters
Scaling and Categorical Variables
Dominant Variables
Problems with Clustering Mixed Dat
Frequently Asked Questions
This "Statistics for Data Scientists" course is an instructor-led training (ILT). The trainer travels to your office location and delivers the training within your office premises. If you need training space for the training we can provide a fully-equipped lab with all the required facilities. The online instructor-led training is also available if required. Online training is live and the instructor's screen will be visible and voice will be audible. Participants screen will also be visible and participants can ask queries during the live session.
Participants will be provided "Statistics for Data Scientists"-specific study material. Participants will have lifetime access to all the code and resources needed for this "Statistics for Data Scientists". Our public GitHub repository and the study material will also be shared with the participants.
All the courses from zekeLabs are hands-on courses. The code/document used in the class will be provided to the participants. Cloud-lab and Virtual Machines are provided to every participant during the "Statistics for Data Scientists" training.
The "Statistics for Data Scientists" training varies several factors. Including the prior knowledge of the team on the subject, the objective of the team learning from the program, customization in the course is needed among others. Contact us to know more about "Statistics for Data Scientists" course duration.
The "Statistics for Data Scientists" training is organised at the client's premises. We have delivered and continue to deliver "Statistics for Data Scientists" training in India, USA, Singapore, Hong Kong, and Indonesia. We also have state-of-art training facilities based on client requirement.
Our Subject matter experts (SMEs) have more than ten years of industry experience. This ensures that the learning program is a 360-degree holistic knowledge and learning experience. The course program has been designed in close collaboration with the experts working in esteemed organizations such as Google, Microsoft, Amazon, and similar others.
Yes, absolutely. For every training, we conduct a technical call with our Subject Matter Expert (SME) and the technical lead of the team that undergoes training. The course is tailored based on the current expertise of the participants, objectives of the team undergoing the training program and short term and long term objectives of the organisation.
Drop a mail to us at [email protected] or call us at +91 8041690175 and we will get back to you at the earliest for your queries on "Statistics for Data Scientists" course.
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