Data Science using Python
DataScience
Data Science is the study of data involving the development of models and methods to juice out the required result. There is an enormous professional opportunity accessible in the field of data science. The vast majority of the up-and-comers are searching for a worthwhile vocation in this specific area. Hence, data science preparation is significant for the freshers and for the experts who are searching for a Data researcher vocation. So as to look at and deal with a colossal arrangement of information through edge open source instruments and information examination calculations, it is required for the possibility to get sufficiently prepared in Data Science. These days there is satisfactory detail accessible which unmistakably shows that there is a shortage of ability to fill the prerequisites of data science experts.
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What you will learn ?
- Introduction to Artificial Intelligence
Get introduced to the Buzz word Artificial Intelligence, Machine Learning and Deep Learning.
- Tools
Anaconda, Spyder, Jupyter Notebook,Virtual Environment.
- Basics of Python
Object Oriented Programming Language, Basics of Python
- Exploratory Data Analysis
Explores the data from different perspectives and tunes them finely thereby giving a cumulative result of the main characteristics obtained by analyzing the data set.
- Descriptive Data Analysis
Brings out the basics of data and by which the same suggests gives the entire description of the data. Under Descriptive Data Analysis, you’ll study various measures, samples and the procedures to work with them in detail.
- Inferential Data Analysis
Specifies the inferences that can be made from and within the data. In easy words, Inferential Data Analysis sorts out the various methodologies in forecasting the data.
- Data Pre-processing
Technique used to transform the unstructured, messy and raw data into structured, neat and understandable format in a step wise manner.
- Data Visualization
Learn the data visualization using Matplotlib, Seborn and Tableau.
Course Outcomes:
- By the end of your happy learning of data science with us,
- You’ll be able to build your programming skills.
- You’ll be able to perform statistical analysis of data.
- You’ll be able to construct your own models and analyze them in real-world contexts.
- You’ll be confident enough to work with the algorithms and tools required for data science.
- You’ll become a pro in data management.
And what not? MUCH MORE!!!!!
Aren’t you enticed to be a DATA SCIENTIST?
Yes, right!!!
We are here to lead you!!!!!
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Course Overview
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Introduction to Artificial Intelligence
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Anaconda Installation
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Basics of Python: Oops
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Exploratory: Descriptive Analysis
- Introduction to Data Science
- Basic Terminology of Data Science: Key Term -1
- Basic Terminology of Data Science: Key Term -2
- Types of Variable
- Concepts under Data Science
- Topics Under Descriptive Analysis
- Data Science: Project Report
- What is outlier?
- What is Mean and Purpose of it
- What is Median and Purpose of it
- Mean vs Median
- Mode
- Python: Dataset Explanation
- Python: Mean, Median and Mode
- Python: Quantitative and Qualitative
- Python Class and Function Fair copy
- Python Custom Table for Mean, Median and Mode
- Percentile and Purpose of it
- Python: Percentile and Lesser and greater range of outlier
- Inter Quartile Range and Outlier
- Python IQR
- Python IQR: Lesser range outlier and Greater range outlier
- Python Fair Descriptive
- Python Detecting Lesser and Greater outlier
- Python Detecting Outlier Fair copy
- Cross Checking for replaced outlier
- Frequency , Relative Frequency and Cumulative Frequency
- Python: Frequency, Relative Frequency and Cumulative Frequency
- Fair Frequency, Relative Frequency
- Variance
- Standard Deviation
- Python Standard Variance
- Skewness and Kurtosis
- Python Skewness, Kurt and Histogram
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Exploratory: Inferential Analysis
- What is Inference Analysis?
- Univariate ,Bivariate and Multivariate
- Concept Under each Analysis
- Co-Variance
- Correlatoion
- Python Covariance, Correlation
- Multicollinearity
- Variance Inflation Factor and Homoscedasticity
- Homoscedasticity and Heteroscedasticity
- Introduction univariate
- Probability Density Curve
- Python PDF
- Python CDF
- Z-Score
- Python Z-Score
- Types of Test
- Paired & Unpaired T-Test
- Python T Test
- F Test
- Hypothesis Testing
- Analysis of Variance and One way Testing
- Python Anavo One Way Testing
- Anavo Two Analysis
- Python Anavo Two way Testing
- Python Two Post hoc
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Data Visualization: Matplotlib
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Data Visualization: Seaborn
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Data Visualization: Tableau
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Data Preprocessing
Instructor
0.00 average based on 0 ratings
Great Explaination of each and every topic👏🏻Understood all the concepts in a easy and clear way.