Data Science Course > Hypothesis Testing

Testing Example in Data Science

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The basic goal of statistics is to prove or disprove a theory:

  • For example, you might conduct research and discover that a particular medicine is useful in the treatment of headaches. No one will believe your findings if you can't repeat the experiment.

Some of the benefits of using Data Science testing include:

  • Informing decisions about product development
  • Increasing customer engagement with products and services
  • Identifying market segments to target with new products or features

Yes, there is. Several data science methodologies (statistics, data mining, web scraping) and programming languages (R, Python) can be applied to software testing as well. Data science and software testing are both empirical research attempting to solve a specific topic.

  • Asking a Question and Researching.
  • Making and Challenging Your Hypothesis.
  • Revising Your Hypothesis.
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Learner's Ratings

4.4

Overall Rating

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Reviews

S
Suman Bhattacharya
4

It's good to understand

V
vaibhav
5

best

A
Anjali
5

Excellent explanation

A
ashif mahiboob yadwad
5

Very good

S
SHAIKH TOHEED
5

good but practical knowledge not perfect

P
PUSHPAK DILLIP MALI
4

The content and teaching style is excellent. But there is slight issue is that video quality is kind of poor the dashboard is blur on 720mp on PC.

S
Sayali Jadhav
4

Very useful all data

S
Syed Mohammad Qaiser Rizvi
5

Way of teaching is very good. I want to get PPT which he is using to teach us ?

A
Anuja Bagadi
5

It is a very interactive and useful course..

K
Kashinath Myakale
5

All courses is very useful for which are looking free courses and gaining knowledge

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  1. Introduction to Data Measurement in Data Science
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  2. Types of Variables in Data Science
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  3. Levels of Data Measurement in Data Science
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  1. Charts and Graphs in Data Science
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  2. Frequency Distribution in Data Science
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  3. Bar Graphs and Pie Charts in Data Science
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  4. Summarizing Quantitative Data in Data Science
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  5. Cummulative Distribution in Data science
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  6. Histogram and Scatter Plot in Data Science
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  7. Cross Tabulation in Data Science
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  1. Measures of Central Tendency and Variability in Data Science
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  2. Mean and Percentiles in Data Science
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  3. Quartiles in Data Science
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  4. Measures of Variability in Data Science
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  5. Variance and Standard Deviation in Data Science
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  6. Application of Standard Deviation in Data Science
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  7. Z-score and Co-efficient of Variation in Data Science
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  8. Measures of Central Tendency for Grouped Data in Data Science
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  9. Standard Deviation and Variance for Grouped Data in Data Science
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  1. Skewness in Data Science
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  2. Kurtosis in Data Science
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  3. Box and Whisker Plots in Data Science
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  1. Hypothesis Testing in Data Science
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  2. Statistical Hypothesis in Data Science
    6m 30s
  3. Errors in Statistical Hypothesis in Data Science
    5m 45s
  4. Statistical Inference for Single Population in Data Science
    7m 11s
  5. Testing Example in Data Science
    5m 27s
  6. Statistical Inference with Two Population in Data Science
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  1. R Programming in Data Science
    5m 5s
  2. Data Types in R in Data Science
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  3. Understanding R Console and R Studio in Data Science
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  1. Analysis of Variance and Annova in Data Science
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  2. Assumption and Variation in One-Way ANOVA in Data Science
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  3. Two way ANOVA in Data Science
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  4. Summary of ANOVA in Data Science
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  5. ANOVA example in Data Science
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  6. One way ANOVA Example in Data Science
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  7. Two way ANOVA Example in Data Science
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  1. Understanding Datamining in Data Science
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  1. Introduction to Simple Linear Regression and Correlation in Data Science
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  2. Least Square and Residual Analysis in Data Science
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  7. Residual Analysis of Model Part 2 in Data Science
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  8. Residual Analysis of Model Part 3 in Data Science
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  1. Understanding Clustering in Data Science
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  2. Hierarchical Clustering in Data Science
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  3. Example of Hierarchial Clustering in Data Science
    13m 39s
  4. K Means Clustering in Data Science
    8m 6s
  5. Example of K Means Clustering in Data Science
    9m 41s

  1. Theoretical Concepts of Dashboard
    5m 41s
  2. Creating Pivots and Preparing Charts
    5m 36s
  3. Slicers in Dashboard
    3m 27s
  4. Preparing Final Dashboard
    8m 20s
  5. Presenting Dashboard
    3m 2s
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