Complete STATISTICS for Data Science | Data Analysis | Full Crash Course

Master Statistics for Data Science & Data Analysis in 4 hours. This comprehensive Crash Course covers EVERYTHING you need to know, from Descriptive Statistics, Probability to Inferential Statistics. Whether you're a complete beginner or looking for a refresher, this video is your one-stop shop for conquering Data Science Statistics!

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Timestamps:
00:00 Syllabus
4:16: Introduction to Statistics
6:39: Descriptive Statistics
9:16: Inferential Statistics
12:45: Types of Data and Variables
21:29: Population and Sample
25:40: Sampling Techniques
33:34: Statistical Analysis
38:12: Measures of Central Tendency
48:18: Measures of Dispersion
1:01:17: Frequency | Relative and Cumulative
1:07:38: Statistical Visualisation
1:25:16: Outliers
1:29:45: Covariance | Correlation | Causation
1:38:56: Probability Concepts (Sample Space | Random Experiment | Event | Complement of Probability)
1:46:24: Types of Events
1:55:30: Conditional Probability
1:58:14: Bayes Theorem
2:06:05: Probability mass function and Probability density function
2:12:20: Bernoulli Distribution | Binomial Distribution
2:19:13: Uniform Distribution
2:22:56: Normal Distribution | Z Distribution
2:27:52: Standardisation | Normalisation
2:36:06: Empirical Rule
2:40:11: Inferential Statistics
2:42:02: Point and Interval Estimation
2:51:30: Confidence Interval
2:59:40: T distribution
3:03:13: Hypothesis Testing
3:06:23: Null and Alternate Hypothesis
3:09:29: Level of Significance | P value
3:12:51: Type | and Type || Error
3:16:29: One tailed and Two tailed Test
3:19:20: Z Test (One sample z test | two sample z test)
3:24:35: T Test (Independent sample t test | Paired sample t test)
3:28:45: ANOVA Test (One way ANOVA | Two way ANOVA)
3:38:26: Chi-Square Test (Independence | Goodness of Fit Test)

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Relevant Keywords:
Statistics Tutorial
Statistics for data science
Statistics for data analysis
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Descriptive statistics
Inferential statistics
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[dipaleegawareC]

00:01 Comprehensive coverage of Statistics for Data Science
02:04 Overview of important statistical concepts
06:36 Descriptive statistics is crucial for understanding data features
09:46 Inferential statistics involves drawing conclusions about a population based on a sample.
14:42 Data collected at specific times and over a sequence of time
17:39 Understanding Nominal, Ordinal, and Numerical Variables in Data Science
22:12 Understanding the concept of population and sample in statistics
25:33 Understanding Sampling Techniques
30:06 Sampling techniques like Systemic, Clustered, and Stratified
32:25 Understanding sampling techniques in statistics
36:39 Importance of interpreting results and documenting analysis process
38:49 Mean, median, and mode calculation in data analysis
43:29 Understanding median and mode in data analysis
45:47 Understanding Mode in Data Analysis
50:59 Understanding the concept of range and quartiles in data analysis
53:17 Understanding Quartiles and Percentiles in Data Analysis
57:26 Variance calculation and its significance
59:32 Standard deviation measures the spread of the data set.
1:03:53 Understanding relative frequency in a complete data set
1:05:51 Frequency table and graphical representation are important for data analysis.
1:10:28 Understanding histograms and their role in data analysis
1:12:46 Understanding Skewed and Symmetric Histograms in Data Analysis
1:17:12 Understanding multimodal data and box plots
1:19:39 Understanding summary statistics and scatter plots
1:24:38 Understanding outliers in data analysis
1:27:02 Identifying and removing outliers in data analysis
1:32:29 Understanding correlations in data analysis
1:35:28 Understanding causation vs. correlation in statistics
1:40:26 Understanding Sample Space and Random Experiment
1:42:55 Calculating probability for rolling a die
1:48:00 Understanding joint and disjoint events in probability theory.
1:50:13 Understanding disjoint events and dependent events in probability.
1:54:53 Conditional probability is based on previous events and outcomes.
1:57:21 Determining the probability of a patient having a specific disease
2:01:45 Understanding confusion matrix for diagnosis in data science
2:04:03 Understanding probability of testing positive for a given condition
2:08:49 Understanding probability mass function and representation in chart form
2:10:43 Understanding Probability Density Function in Data Analysis
2:14:47 Bernoulli distribution explained
2:17:05 Calculation of probability of visitors making a purchase
2:21:22 Uniform distribution is used in predicting arrival times of customers.
2:23:15 Understanding Normal Distribution in Data Analysis
2:27:33 Z scores tell us how many standard deviations from the mean each value lies.
2:29:37 Normalization and Scaling in Data Analysis
2:33:35 Normalization vs. Standardization for Data Distribution
2:35:21 Understanding Standard Normal Distribution
2:39:56 Inferential statistics help in making predictions about the population based on a sample.
2:41:47 Understanding population parameters vs sample statistics
2:45:55 Importance of Sample Size in Accurate Estimation
2:48:06 Key concepts of point and interval estimation in statistics
2:52:40 Confidence intervals provide estimation with a specified level of confidence
2:54:46 Understanding confidence intervals in statistics
2:59:00 Knowing when to use z distribution or t distribution is crucial for statistical analysis.
3:01:11 Importance of sample size in determining standard deviation
3:05:18 Understanding point estimation interval and hypothesis testing
3:07:06 Hypothesis testing involves null and alternative hypotheses
3:11:55 Hypothesis testing decision rule
3:14:33 Type one and type two errors in hypothesis testing.
3:18:48 Understanding critical regions in hypothesis testing
3:20:49 Understanding Standard Deviation and its application in statistical testing
3:25:25 T test compares means of independent and paired groups
3:27:40 Explaining Paired T-Test & Anova Test
3:32:10 Understanding the significance of variables in data analysis
3:34:21 Understanding factors and response variables in data analysis
3:39:17 Chi square test for comparing and testing independence between categories.
3:41:16 Understanding chi square test and its formulas
3:45:32 Statistics is essential for data science and data analysis
Crafted by Merlin AI.

[Adnan.]

Statistics for data analytics:

### 1. **Basic Concepts**
- **Descriptive Statistics**:
- Mean, Median, Mode
- Variance, Standard Deviation
- Skewness, Kurtosis
- Data Visualization: Histograms, Bar Charts, Box Plots
- **Probability Fundamentals**:
- Probability Theory
- Conditional Probability
- Bayes’ Theorem
- Random Variables
- Probability Distributions (e.g., Normal, Binomial, Poisson)

### 2. **Intermediate Concepts**
- **Inferential Statistics**:
- Population vs. Sample
- Central Limit Theorem
- Confidence Intervals
- Hypothesis Testing (t-test, chi-square test, ANOVA)
- p-values and Statistical Significance
- **Regression Analysis**:
- Linear Regression
- Multiple Regression
- Logistic Regression

### 3. **Advanced Topics**
- **Multivariate Statistics**:
- Principal Component Analysis (PCA)
- Factor Analysis
- Cluster Analysis (K-means, Hierarchical)
- Dimensionality Reduction Techniques
- **Time Series Analysis**:
- Components of Time Series
- Moving Averages
- ARIMA Models
- Seasonal Decomposition

### 4. **Machine Learning Applications**:
- **Supervised Learning**:
- Decision Trees, Random Forests
- Support Vector Machines (SVM)
- Neural Networks
- **Unsupervised Learning**:
- Clustering Techniques
- Anomaly Detection
- **Model Evaluation**:
- Cross-Validation
- Bias-Variance Tradeoff
- ROC Curves, AUC

### 5. **Practical Applications**:
- **Data Cleaning and Preprocessing**:
- Handling Missing Data
- Outlier Detection
- Feature Scaling and Normalization
- **Real-World Data Analysis**:
- Case Studies and Projects
- Working with Large Datasets (e.g., Pandas, SQL)
- Data Visualization Tools (e.g., Matplotlib, Seaborn, Tableau)

### 6. **Advanced Statistical Programming**:
- **R for Statistical Analysis**:
- Basics of R Programming
- Data Manipulation (dplyr, tidyr)
- Statistical Modeling in R
- **Python for Data Analysis**:
- Libraries: NumPy, Pandas, SciPy, Statsmodels
- Data Visualization: Matplotlib, Seaborn
- Implementing Statistical Tests and Models in Python

### 7. **Continuous Learning**:
- **Online Courses and Resources**:
- Coursera, edX (Courses on Statistics, Data Science)
- Books: "Practical Statistics for Data Scientists, " "An Introduction to Statistical Learning"
- **Practice with Real Data**:
- Kaggle Competitions
- GitHub Projects
- **Stay Updated**:
- Follow blogs, attend webinars, and read research papers in the field of data analytics and statistics.

### 8. **Certification and Specialization**:
- Consider certifications like Google Data Analytics, SAS Certified Statistical Business Analyst, or more specialized ones in machine learning and statistics.

[Motivational_Greek]

In the time stamp 3.08.36, Null hypothesis should be mean=<170 and alternative = mean >170

[sarojmaharjan]

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[souvik]

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[sohaibalam]

As an incoming MSc Data science student in Germany, this video is really helpful. Thankyou ma'am ❤️

[anurag]

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The visual aids and graphics used throughout the video were top-notch. They significantly enhanced my comprehension by illustrating the concepts in a dynamic and visually appealing way. The step-by-step explanations of statistical procedures, such as hypothesis testing and regression analysis, were particularly helpful. They not only provided a clear roadmap for performing these analyses but also highlighted common pitfalls and how to avoid them.

I also appreciated the practical applications and real-world examples that were seamlessly integrated into the tutorial. These examples helped bridge the gap between theory and practice, demonstrating how statistical methods can be applied to solve real-life problems. The instructor's enthusiasm and passion for the subject were evident and infectious, making the learning experience enjoyable and motivating.

Overall, this statistics video is an invaluable resource for anyone looking to deepen their understanding of statistics, whether for academic, professional, or personal enrichment. The thoughtful presentation and comprehensive coverage of topics make it a standout educational tool that I highly recommend.

[vishalkhare]

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[shauryajain]

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[Jerrel.A]

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[omarisawesome]

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[hamidraza]

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[wiqarali]

Simple and most effective explanation of Statistics - covering almost all the key concepts (great review in a single shot)

[vivekmartin]

ma'am can't thank you enough Such difficult topics are explained in such a way that everyone can understand it clearly. May God bless

[gryffinpuff_]

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[PatialaKing-upte]

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[Adnan.]

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[alihussien]

I wish it was in English it's sounds you explain everything Easy

[ogmjvfw]

Thanks Di For Uploading This Tutorial. Bohot Easy Way Me Explain Kiya He Aapne.