Statistics for Data Scientists-training-in-bangalore-by-zekelabs

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-training-in-bangalore-by-zekelabs
Statistics for Data Scientists-training-in-bangalore-by-zekelabs
Industry Level Projects
Statistics for Data Scientists-training-in-bangalore-by-zekelabs

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
Further Reading
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
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
Type 1 and Type 2 Errors
Further Reading
Further Reading
Further Reading
Further Reading
Further Reading
Chi-Square Test: A Resampling Approach
Fisher’s Exact Test
Further Reading
Further Reading
Sample Size
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
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
Further Reading
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
Variable Importance
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

We have options for classroom-based as well as instructor led live online training. The online training is live and the instructors screen will be visible and voice will be audible. Your screen will also be visible and you can ask queries during the live session.

The training on "Statistics for Data Scientists" course is a hands-on training. All the code and exercises will be done in the live sessions. Our batch sizes are generally small so that personalized attention can be given to each and every learner.

We will provide course-specific study material as the course progresses. You will have lifetime access to all the code and basic settings needed for this "Statistics for Data Scientists" through our GitHub account and the study material that we share with you. You can use that for quick reference

Feel free to drop a mail to us at [email protected] and we will get back to you at the earliest for your queries on "Statistics for Data Scientists" course.

We have tie-ups with a number of hiring partners and and placement assistance companies to whom we connect our learners. Each "Statistics for Data Scientists" course ends with career consulting and guidance on interview preparation.

Minimum 2-3 projects of industry standards on "Statistics for Data Scientists" will be provided.

Yes, we provide course completion certificate to all students. Each "Statistics for Data Scientists" training ends with training and project completion certificate.

You can pay by card (debit/credit), cash, cheque and net-banking. You can also pay in easy installments. You can reach out to us for more information.

We take pride in providing post-training career consulting for "Statistics for Data Scientists".

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