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- Prediction analysis using various latest coding languages
- Python
- R
- Machine learning
- Data visualization
- Big data management
- Natural language processing

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- Data Analyst
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- Product analyst.

In the sampling process, there are three types of biases, which are:

- Selection bias
- Under coverage bias
- Survivorship bias

Resampling is done in below-given cases:

- Estimating the accuracy of sample statistics by drawing randomly with replacement from a set of the data point or using as subsets of accessible data
- Substituting labels on data points when performing necessary tests
- Validating
**models by using random subsets**

Three disadvantages of the linear model are:

- The assumption of linearity of the errors.
- You can't use this model for binary or count outcomes
- There are plenty of overfitting problems that it can't solve

- SciPy
- Pandas
- Matplotlib
- NumPy
- SciKit
- Seaborn

- What is Python?
- Why Python?
- Installation of python
- Conditions
- Loops
- Break statement
- Continue statement
- Range functions
- Command line arguments

- String Object Basics
- String Methods
- Splitting and Joining Strings
- String format functions
- List Object Basics
- List Methods
- Tuples
- Sets
- Frozen sets
- Dictionary
- Iterators
- Generators
- Decorators
- List Set Dictionary comprehensions

- Creating Classes and Objects
- Inheritance
- Multiple Inheritance
- Working with files
- Reading and Writing files
- Using Standard Modules
- Creating custom modules
- Exceptions Handling with Try-except
- Finally, in exception handling

- ND array Object
- Data Types
- Array Attributes
- Array Creation Routines
- Array from Existing Data
- Array from Numerical Ranges
- Indexing & Slicing
- Advanced Indexing
- Broadcasting
- Iterating Over Array
- Array Manipulation
- Binary Operators
- String Functions
- Mathematical Functions
- Arithmetic Operations
- Statistical Functions
- Sort, Search & Counting Functions
- Byte Swapping
- Copies & Views
- Matrix Library

- Series
- Data Frame
- Panel
- Basic Functionality
- Re indexing
- Iteration
- Sorting
- Working with Text Data
- Options & Customization
- Indexing & Selecting Data
- Window Functions
- Date Functionality
- Time delta
- Categorical Data
- Visualization
- IO Tools

- Introduction & Installation
- Format strings in plot function
- Axes labels
- Legend
- Grid
- Bar chart
- Histograms
- Pie chart
- Save fig
- Scatter plots
- Sub plots

- Introduction & Installation
- Bar plot
- Distributed plot
- Box plot
- Strip plot
- Pair grid
- Violin Plot
- Cluster Map
- Heat Map
- Facet Grid
- KDE plot
- Joint plot
- Reg plot
- Pair plot

- Numerical variables
- Categorical variables
- Missing Values
- Outliers
- Mean and median imputation
- Random sample imputation
- Dummy variables
- One hot encoding
- Train and test data split
- Save model using pickle

- Descriptive Statistics
- Sample vs Population
- Random Variables
- Probability Distribution function
- Expected value
- Binomial Distribution
- Normal Distributions
- Z-score
- Central limit Theorem
- Hypothesis testing
- Z-Stats vs T-stats
- Type 1 & Type 2 error
- Confidence Interval
- Chi Square test
- ANOVA test
- F-Stats

- Analyzing Bike Sharing Trends
- Analyzing Movie Reviews Sentiment
- Customer Segmentation and Effective Cross Selling Analyzing Wine Types and Quality
- Analyzing Music Trends and Recommendations Forecasting Stock and Commodity Prices

- What is Machine Learning
- Machine Learning Types
- Supervised learning
- Unsupervised learning
- Reinforcement learning
- Deep learning
- Linear regression
- Multiple linear regression
- Gradient Descent
- Ridge regression
- Lasso regression
- Logistic regression-Binary classification
- Logistic regression-Multi Class classification
- K Nearest Neighbors (KNN)
- Naive Bayes
- Decision trees
- Random forests
- Un Supervised Learning
- K Means Clustering

- K fold cross validation
- Hyper parameter tuning
- Grid Search CV
- Randomized CV
- Ensemble Methods
- Boosting
- Bagging

- Introduction
- Sign up for AWS account
- Setup Cygwin on Windows
- Quick Preview of Cygwin
- Understand Pricing
- Create first EC2 Instance
- Connecting to EC2 Instance
- Understanding EC2 dashboard left menu
- Different EC2 Instance states
- Describing EC2 Instance
- Using elastic IPs to connect to EC2 Instance
- Using security groups to provide security to EC2 Instance
- Understanding the concept of bastion server
- Terminating EC2 Instance and relieving all the resources
- Create security credentials for AWS account
- Setting up AWS CLI in Windows
- Creating s3 bucket
- Deleting root access keys
- Enable MFA for root account
- Introduction to IAM users and customizing sign in link
- Create first IAM user
- Create group and add user
- Configure IAM password policy
- Understanding IAM best practices
- AWS managed policies and creating custom policies
- Assign policy to entities (user and/or group)
- Creating role for EC2 trusted entity with permissions on s3
- Assigning role to EC2 instance
- Deploying Machine Learning Model to AWS

- 3 Real-Time Projects
- Deployment on multiple platforms
- Discussion on project explanation in interview
- Data scientist roles and responsibilities
- Data scientist day to day work
- One to One resume Discussion with project, technology and Experience.
- Mock interview for every student
- Real time Interview Questions