a) Descriptive Statistics:

• Central tendency: Mean, Median, Mode
• Sample variance
• Standard deviation
• Random Variables: Discrete, Continuous
• Probability density functions
• Binomial distribution
• Expected Value, E(X)
• Poisson Process
• Law of large numbers
• Standard normal distribution and empirical rule
• Z-score

b) Inferential Statistics:

• Central limit theorem
• Sampling distribution of the sample mean
• Standard error of the mean
• Mean and variance of Bernoulli distribution
• Margin of error 1
• Margin of error 2
• Confidence interval
• Hypothesis testing and p-value
• One-tailed and two tailed tests
• Z-statistics and T-statistics
• Type 1 error
• Squared error of regression line
• Co-efficient of determination
• Chi-square distribution
• Pearson’s chi square test (goodness of fit)
• Co-relation and casualty.

1. Numpy

• Numpy Numpy Vector and Matrix
• Functions – arrange(), zeros(), ones(), linspace(), eye (),
• Reshape(), random(), max(), min(),
• argmax(), argmin(), shape and dtype attribute
• Indexing and Selection
• Numpy Operations – Array with Array, Array with Scalars,
• Universal Array Functions

2.Pandas

• Pandas Series
• Pandas Data-Frame
• Missing Data (Imputation)
• Group by Operations
• Merging, Joining and Concatenating Data-Frame.
• Pandas Operations
• Data Input and Output from wide variety of formats like csv, excel, db and html etc.

1.Matplotlib

• plot() using Functional approach
• multi-plot using subplot()
• plt.figure() using OO API Methods
• add_axes(), set_xlabel(), set_ylabel(), set_title() Methods
• Customization – figure size, impoving dpi, Plot appearance,
• Markers, Control over axis appearance and special Plot Types

2.Seaborn

• Distribution Plots using distplot(), jointplot(), pairplot(), rugplot(),
• kdeplot()
• Categorical Plots using barplot(), countplot(), boxplot(), violinplot(),
• stripplot(), swarmplot(), factorplot()
• Matrix Plots using heatmap(), clustermap()
• Grid Plots using PairGrid(), FacetGrid()
• Regression Plots using lmplot()
• Styles and Colors customization.

3. Plotly and Cufflinks

• Interactive Plotting using Plotly and Cufflinks

4.Pandas Built-in

• Histogram, Area Plot, Bar Plot, Scatter Plot, Box-plot, Hex-plot, Kde-plot, Density Plot e. Choropleth Maps
• Interactive World Map and US Map using Plotly and Cufflinks Module

• Distribution Version Control System
• How internally, GIT Manages Version Control on Changesets.
• Creating Repository
• Basic Commands like, git status, git add, git remove, git branch, git checkout, git log, git cat-file, git pull, git push, git commit
• Managing Configuration – System Level, User Level, Repository level

• Introduction, Basic Commands, Keyboard Shortcut and Magic Functions

• Vector and Matrix, basic operations
• Trigonometry
• Derivatives

• MySQL Server and Client Installation
• SQL Queries
• CRUD Operations

• What is big data?
• What is distributed computing?
• What is parallel processing?
• Why data scientist require big data?

• What is Machine Learning
• Machine Learning Process Flow-Diagram
• Different Categories of Machine Leaning – Super- vised, Unsupervised and Reinforcement
• Scikit-Learn Overview
• Scikit-Learn cheat-sheet

• Linear Regression
• Robust Regression (RANSAC Algorithm)
• Exploratory Data Analysis (EDA)
• Correlation Analysis and Feature Selection
• Performance Evaluation – Residual Analysis, Mean Square Error (MSE), Co-efficient
• Determination R^2, Mean Absolute Error (MAE), Root Mean Square Error (RMSE)
• Polynomial Regression
• Regularized Regression – Ridge, Lasso and Elas- tic Net Regression
• Bias-Variance Trade-Off
• Cross Validation – Hold Out and K-Fold Cross Validation
• Data Pre-Processing – Standardization, Min-Max, Normalization and
• Binarization
• Gradient Descent

Projects

1. Predicting Boston House Prices – https://www.kaggle.com/schirmerchad/ bostonhoustingmlnd
2. Ecommerce Project – Company want to decide whether to focus their efforts on Mobile Experience or Website Experience.
3. 4 USA Housing Prediction Project.
4. New York City Taxi Fare Prediction – https://www.kaggle.com/c/new-york-city-taxi- fareprediction
5. Emergency 911 Calls – https://www.kaggle.com/ mchirico/montcoalert

• Classification and Regression
• Application, Advantages and Disadvantages
• Distance Metric – Euclidean, Manhattan, Cheby- shev, Minkowski
• Measuring accuracy using Cross-Validation, Stratified k-fold, Confusion Matrix, Precision, Recall, F1-score.

Projects

1. Breast Cancer Wisconsin (Diagnostic) Pro-ject using KNN- https://www.kaggle.com/uciml/breastcancer-wisconsin-data
2. Iris Species – https://www.kaggle.com/ uciml/iris

• Classification and Regression
• Separating line, Margin and Support Vectors
• Linear SVC Classification
• Polynomial Kernel – Kernel Trick
• Gaussian Radial Basis Function (rbf)
• Grid Search to tune hyper-parameters
• Support Vector Regression

Projects

1. Breast Cancer Wisconsin (Diagnostic) Project using KNN –https://www.kaggle.com/uciml/breastcancer-wisconsin-data
2. Iris Species – https://www.kaggle.com/uciml/iris

• CART (Classification and Regression Tree)
• Advantages and Disadvantages and its applications.
• Decision Tree Learning algorithms – ID3, C4.5, C5.0 and CART.
• Gini Impurity, Entropy and Information Gain
• Decision Tree Regression
• Visualizing a Decision Tree using graphviz module.
• Regularization using tuning hyper-parameters using GridSearch CV.

Projects

1.IBM HR Analytics Employee Attrition and Per-formance –

https://www.kaggle.com/pavansubhasht/ibm-hranalytics-attritiondatasetZomato

2.Restaurants Data – https://www.kaggle.com/shrutimehta/zomatorestaurants-data

3.Predicting Bank Marketing Analysis -https://www.kaggle.com/kevalm/bankmarketingdataset

4.FIFA 18 Complete Player Dataset – https://www.kaggle.com/thec03u5/fifa-18demo-

playerdataset

• Bootstrap Aggregating or Bagging
• Random Forest algorithm
• Extremely Randomized (Extra-Trees) Ensemble
• Boosting – AdaBoost (Adaptive Boosting), Gradient Boosting
• Machine (GBM), XGBoost (Extreme Gradient Boosting)

• Connectivity- based Clustering using Hierarchical Clustering.
• Ward’s Agglomerative Hierarchical Clustering
• K-Means Clustering
• Elbow Method and Solhouette Analysis

Projects

1. Lending Club Loan Data Analysis – https://www.kaggle.com/wendykan/lending-club-loan- data

2.U.S. News And World Report’s College Data –https://www.kaggle.com/flyingwombat/us-newsand- world-reports-college-data

3.Credit Card Dataset for Clustering –https://www.kaggle.com/arjunbhasin2013/ccdata

• Linear Principal Component Analysis (PCA) reduction.
• Kernel PCA
• Linear Discriminant Analysis (LDA) on Supervised Data.

Projects

1.Breast Cancer Wisconsin (Diagnostic) Analysis us-ing PCA –

https://www.kaggle.com/uciml/breast-cancerwisconsin -data

2.Predicting Abalone’s Sex – https:// www.kaggle.com/yuridias/abalonedataset

3.Wine Project – https://www.kaggle.com/zynicide/ wine-reviews

4.SMS Spam Collection Dataset Analysis –https://www.kaggle.com/uciml/sms-spam- collection- dataset

5.Auto Summarizing Text using Rule Based Model.

6.Yelp Business Rating Prediction – https:// www.kaggle.com/c/yelprecsys-2013

• What is cloud computing?
• What is AWS?
• How to store data in AWS S3?
• Create deep learning instance on EC2.
• Amazon sage maker to train, tune, build and deploy on production.

“Our Data Science course equips you with the skills to extract meaningful insights from data, making informed decisions and driving business growth.” “Join our Data Science course to master statistical analysis, machine learning, and data visualization, preparing you for a rewarding career in data-driven industries.”

obtaining proficiency in data analysis, machine learning, and statistical modelling. empowering students to overcome difficult business challenges, succeed in data-related jobs across multiple industries, and make data-driven decisions.

Gaining knowledge of data science is advantageous as it enables individuals to utilise data to resolve intricate issues, make informed choices, and acquire profound comprehension. This skill is highly valued in a variety of industries and can result in fulfilling job prospects, growth, and innovation.

This course is suitable for anyone who wants to work in the data-driven industry, regardless of whether they are an analyst, data scientist, business professional, or career changer. There are many different backgrounds and interests among those who can pursue training in data science.

We provide you the chance to work on practical projects where you can put the knowledge and abilities you learned in our training to use. Our numerous projects will put your abilities and understanding of different Hadoop components to the test, ensuring that you are fully prepared for the business. These projects could be in demanding and fascinating industries like high technology, banking, insurance, retail, social networking, and so on. Six months of appropriate work experience in a corporate setting is equivalent to the Prwatech projects.

You can open a ticket with our dedicated 24/7 help if you have any questions. We answer your questions and offer email help. If the email does not answer your question, we can even set up a one-on-one meeting with one of our trainers. The best thing is that you may still obtain support and help from Prwatech even after the training is over by contacting them. When it comes to doubt clearing and query resolution, there is also no cap on the quantity of queries you may pose.

Prwatech primarily provides online instructor-led training and self-paced training. In addition, we provide businesses corporate training. All of our trainers are subject matter experts who work as consultants and have over five years of expertise in the business with relevant technology. The sample videos that are offered will allow you to evaluate the calibre of our teachers.