Data Science
BEGINNER TO ADVANCE
DATA SCIENCE COMPLETE COURSE
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"Its basic to advance. Being a complete beginner, it was really helpful..."
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What exactly do I get in this?
- Introduction
- What is Big Data and for what reason does it make a difference?
- Applications of big data across industry areas
- Data researcher
- The primary innovative parts in a Big Data environment
- Summary
- Business analytics
- Incorporating Data-Driven Insights into the Business Process
- Deploying analytics and data wrangling to convert raw data into actionable insights
- Distinguishing Business Intelligence and Data Science
- Knowing Who to Call to Get the Job Done Right
- Data analytics
- Data
- Subjective data
- Data Strategies
- Imagining Data
- The "Issue" with Data Analysis
- Data Analysis as a Linear Process
- Data Analysis as a Cycle
- Data science
- What is data science?
- Where data comes from
- Working with data at scale
- Techniques for working with Traditional Data
- Introduction
- Traditional data storage and processing methods
- Application and performance analysis
- Conclusions
- Business Intelligence (BI) Techniques
- Introduction
- Business Intelligence
- Parts of BI
- Issues In Bi
- Eventual fate of business Insight
- Purposes behind business knowledge
- Advantages of BI
- Business Intelligence Technology
- Conclusion
- Machine Learning (ML) Techniques
- Supervised Learning Approach
- Unsupervised learning
- Algorithm Types
- Necessary Programming Languages and Software Used in Data Science
- Introduction
- Popularity
- Library Support
- Community
- OS apparatus recognizable proof
- Proposed grouping plan qualities
- Proposed grouping plan
- Conclusion
- Probability, Frequency, Events and Their Complements
- Probability
- Bayes' Theorem
- Descriptive Statistics
- Proportions of Center: Mean, Median, Mode, Mid-range
- Proportions of Variability: Range, Variance, Standard Deviation
- Proportions of Position: Percentile, Z-score, Quartiles
- Conclusion
- Permutations and How to Use Them
- Introduction
- History
- Notation
- Different employments of the term permutation
- Symmetry of Combination
- Introduction
- Neural Machine Translation
- Neural System Combination for Machine Translation
- Experiments
- Related Work
- Conclusion and Future Work
- Sets and Events, Bayes' Law
- Introduction
- Sets and Events
- Methodology
- A Counting Heuristic
- Relating the Counting Heuristic to Bayes' Rule
- Further Reading
- Conclusion
- Probability Distributions
- General Properties of Probability Distributions
- Discrete Probability Distributions
- Kinds of Discrete Distribution
- Illustration of How to Use Discrete Probability Distributions
- Constant Probability Distributions
- Attributes of Continuous Probability Distributions
- Illustration of Using the Normal Probability Distribution
- Illustration of Using the Lognormal Probability Distribution
- Theory Testing Uses Special Probability Distributions
- Conclusion
- Types of Data, Measurement, Histogram, Mean, Median and Mode, Skewness, Variance, Covariance
- Trendy expressions
- Types of data
- Types of Data Science
- Histogram, Skewness, Mean, Median, and Mode
- Variance versus Covariance: An Overview
- Conclusions
- Distribution, Normal Distribution, Standard Normal Distribution, Central Limit Theorem, Standard error, Estimators and Estimates
- Introduction
- Distribution
- Understanding Central Limit Theorem
- What is an Estimate and Estimator?
- Confidence Intervals, Student's T Distribution, Margin of Error
- Introduction
- What is T Distribution?
- What is a Margin of Error?
- Null vs Alternative Hypothesis, Rejection Region, and Significance Level Type I Error and Type II Error, Test for the Mean. Population Variance Known, p-value
- Introduction
- Hypothesis Testing: Upper-, Lower, and Two-Tailed Tests
- Type I and Type II Errors
- Tests with One Sample, Continuous Outcome
- Conclusion
- Python
- Presenting data science and Python
- Scientific distributions
- A look at the fundamental bundles
- Downloading the model code
- Presenting Jupyter
- Data Munging
- Data preprocessing
- Data determination
- Working with categorical and text data
- Controlling the memory size
- Summary
- Deep learning
- Introduction
- General Theory of Data Science
- Theory of Data Science Process
- General Theory of Artificial Intelligence
- Theory of Data
- Data Extract, Transformation and Loading (ETL)
- Theory of Deep Learning
- Deep Learning Algorithms Cheat Sheet
- and much more..
Video Lessons Includes:
- Python
- Introduction to python
- Datatypes, Variables, Strings
- Loops, List, Functions, Tuples, Dictionaries
- Assignments on python, Interview questions on python, Use of Python
- Data Analysis
- Understanding the basic libraries required for data analysis and data science in detail
- How to import dataset
- Data preprocessing
- Data Visualization and Exploratory
- Data Analysis using 7-8 different graphs and plots
- Interview questions, mini project and use of data analysis in data science and career data analysis
- Machine Learning Algorithms for Data Science
- KNN Classification
- Decision tree classification
- Random forest classification
- Linear regression
- Interview questions on data science, job opportunities in data science
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