Course Overview

In this Specialization, training will develop foundational Data Science abilities to prepare them for a career or further online training that involves more complex subjects in Data Science. The specialty entails understanding what is Data Science and the various kinds of actions that a Information Scientist performs. It will familiarize students with various open source programs, like Jupyter laptops, used by Data Laboratory. This will teach you about methodology involved in handling data science problems. The specialization also provides knowledge of relational database concepts and the use of SQL to query databases. Learners will complete hands-on labs and jobs to apply their newly acquired skills and knowledge.

A Bestway Specialization is a collection of classes that help you master a skill. To start, enroll in the Specialization right, or examine its courses and pick the one you'd love to start with. When you sign up for a course that's part of a Specialization, you're automatically subscribed to the full Specialization. It is okay to complete just 1 course -- you can pause your understanding or end your subscription at any time. Stop by your learner dashboard to track your course enrollments and your own progress.

Bestway Data Science Online Training course supplies you with an entire package to be a knowledgeable Data Scientist. Besides, you will also gain practical knowledge by implementing real-time jobs and supplying solutions to the issues. In a nutshell, this Information Science online training makes you completely confident in facing interviews and working as an Information Scientist

Data Science class content in Bestway was created by professional writers who hold company real-time knowledge in artificial intelligence, machine learning, deep learning, and several other newest technologies. Our newest 2020 Information Science course program focuses on existing industry requirements and can help one to successfully decode data interviews and update your career course. You will learn the whole data science syllabus below the following sections

Topics covered in this section are:

• Data science life cycle
• Significance of Data Science Inside This data-driven world
• Programs of Data Science
• Introduction to large data and Hadoop
• Introduction to machine learning, deep learning, R programming, and R Studio

Learning outcome: From the end of this session, you'll obtain complete knowledge of how Data Science works in real-time and installation of R studio on your machine. You'll also become familiar with simple calculations and logic utilizing R loops, operators, and buttons.

2. Data Exploration

Data Exploration segment is among the crucial issues of Data Science training. Data exploration is an approach that's similar to the initial data analysis where a data analyst utilizes it to understand what a data set is and understand the figures that a dataset contains.

3. Data Visualization

Data visualization is the inner and crucial part of Data Science. This section will help you to learn how to extract the hidden tendencies out of information and represent them in the form of graphs and charts.

4. Statistics

Statistics is an essential component of data science and plays a significant role within it. Multiple statistical approaches available are regression, classification, time series and theory testing; info scientists use all the approaches to run suitable experiments and to outline the information fairly & quickly.

Hands-on Project

Every Specialization includes a hands on project. You will need to successfully complete the project(s) to successfully complete the Specialization and make your certification. If the Specialization comprises another course for the hands-on job, you'll want to finish every one of the other classes before you are able to begin it.

Make a Certificate

When you Finifinishry class and fill out the hands-on project, you'll earn a Certificate which you can share with prospective employers along with your professional community.

Course Curriculum

DATA SCIENCE COURSE CONTENT:

 

Introduction to BIG Data Science/Data Analytics 

What background is required?

What is Data Science?

Why Data Science?

BIG Data Science/Analytics trend

What is Machine Learning?

Data Science Life Cycle

 

Tools for Data Science/Analytics

 Anaconda Distribution package

Open Source: Python/R

Visualization tools: Matplotlib,  introduction of Tableau

 

Data Analytics Problems/Use-cases

 From Kaggle competitions

Types of Data: Structured, Unstructured (Image, Text…..)

Predictive Analytics Problems: Classification, Regression, Recommenders

Descriptive Analytics Problems: Clustering, Market Basket Analysis, PCA

Business Verticals: Retail, Real Estate, Banking, Financial, Social, Web, Medical, Scientific, Logistics

 

Visualization tools:

Matplotlib,

Introduction of Tableau

 

Statistics for Data Scientist

Descriptive Statistics for single variables

Mean, Median, Mode, Quartile, Percentile

Interquartile Range

Standard Deviation

Variance

Descriptive Statistics for two variables

Z-Score

Co-variance

Co-relation

Chi-squared Analysis

Hypothesis Testing

 

Calculus for Data Scientist

Limits

Derivatives

Partial Derivatives

Gradients

Significance of Gradients

 

Probability for Data Scientist

Basic Probability

Conditional Probability

Properties of Random Variables

Expectations

Variance

Entropy and cross-entropy

Covariance and correlation

Estimating probability of Random variable

Understanding standard random processes

 

Data Distributions

Normal Distribution

Binomial Distribution

Multinomial Distribution

Bernoulli Distribution

Probability, Prior probability, Posterior probability

Bayes Theorem

Naive Bayes

Naive Bayes Algorithm

Normal Distribution

 

Mastering Python/R Language

How to install python (Anaconda)

How to install sciKit Learn (Anaconda)

How to work with Jupyter Notebook

How to work with Spyder IDE

Strings

Lists

Tuples

Sets

Dictionaries

Control Flows

Functions

Formal/Positional/Keyword arguments

Predefined functions (range, len, enumerates etc…)

Data Frames

Packages required for data Science in R/Python

Lab/Coding

 

Introduction to NumPy

One-dimensional Array

Two-dimensional Array

Pre-defined functions (arrange, reshape, zeros, ones, empty)

Basic Matrix operations

Scalar addition, subtraction, multiplication, division

Matrix addition, subtraction, multiplication, division and transpose

Slicing

Indexing

Looping

Shape Manipulation

Stacking

 

Introduction to Pandas

Series

DataFrame

GroupBy

crosstab

apply

map

 

Decision Trees

What are Decision Trees?

Gini, Entropy criterions

Decision trees in Classification

Decision trees in Regression

Ensembles

Random Forest

Boosting (Ada, Gradient, Extreme Gradient)

SVM

Ensembles

 

Overfitting/Under fitting

Understand what is overfitting and under fitting model

Visualize the overfitting and under fitting model

How do you handle overfitting?

 

 Data Preparation Techniques

Structured Data Preparation

Data Type Conversion

Category to Numeric Conversion

Numeric to Category Conversion

Data Normalization: 0-1, Z-Score

Handling Skew Data: Box-Cox Transformation

Handling Missing Data

 

Re-sampling Techniques

K-fold

Repeated Hold-out Data

Bootstrap aggregation sampling

 

Exploratory Data Analysis (EDA)

Statistical Data Analysis

Data Visualization (Matplotlib, Seaboarn)

Exploring Individual Features

Exploring Bi-Feature Relationships

Exploring Multi-feature Relationships

Feature/Dimension Reduction: PCA

Intuition behind PCA

Covariance & Correlation

Relating PCA to Covariance/Correlation

Intuition to math

Applications of PCA: Dimensionality Reduction

 

Feature Engineering (FE)

Combine Features

Split Features

 

Data Visualization

Bar Chart

Histogram

Box whisker plot

Line plot

Scatter Plot

Heat Map

 

 Tree Based Algorithms

 Gini Index

Entropy

Information Gain

Tree Pruning

 

Classification (Supervised Learning)

What is Classification?

Finding Patterns/Fixed Patterns

Problems with Fixed Patterns

Machine learning approach over fixed pattern approach

Decision Tree based classification

Ensemble Based Classification

Logistic Regression (SGD Classifier)

Accuracy measurements

Confusion Matrix

ROC Curve

AUC Score

Multi-class Classification

Softmax Regression Classifier

Multi-label Classification

Multi-output Classification

 

Ensemble models

Random Forest

Bagging

Boosting

Adaptive Boosting

Gradient Boosting

Extreme Gradient Boosting

Heterogeneous Ensemble Models

Stacking

Voting

 

Regression (Supervised Learning)

What is regression?

Regression example in business verticals

Solution strategies for Regression

Linear Regression

Explanation of statistics

Evaluation metrics

Root Mean Squeare(RMSE)

R-Squre,

Adj R-Squre

Feature selection methods

Linear regression

 

Multiple/Polynomial Regression (scikit-learn)

Multiple Linear Regressions (SGD Regressor)

Gradient Descent (Calculus way of solving linear equation)

Feature Scaling (Min-Max vs Mean Normalization)

Feature Transformation

Polynomial Regression

Matrix addition, subtraction, multiplication and transpose

Optimization theory for data scientist

 

Optimisation Theory (Gradient Descent Algorithm)

Modelling ML problems with optimization requirements

Solving unconstrained optimization problems

Solving optimization problems with linear constraints

Gradient descent ideas

Gradient descent

 

Model Evaluation and Error Analysis

Train/Validation/Test split

K-Fold Cross Validation

The Problem of Over-fitting (Bias-Variance tread-off)

Learning Curve

Regularization (Ridge, Lasso and Elastic-Net)

Hyper Parameter Tuning (GridSearchCV)

 

Recommendation Problem

What is Recommendation System?

Top-N Recommender

Rating Prediction

Content based Recommenders

Limitations of Content based recommenders

Machine Learning Approaches for Recommenders

User-User KNN model, Item-Item KNN model

Factorization or latent factor model

Hybrid Recommenders

Evaluation Metrics for Recommendation Algorithms

Top-N Recommnder: Accuracy, Error Rate

Rating Prediction: RMSE

 

Clustering (Unsupervised Learning)

Finding pattern and Fixed Pattern Approach

Limitations of Fixed Pattern Approach

Machine Learning Approaches for Clustering

Iterative based K-Means Approaches

Density based DB-SCAN Approach

Evaluation Metrics for Clustering

Cohesion, Coupling Metrics

Correlation Metric

 

Support Vector Machine (SVM)

SVM Classifier (Soft/Hard – Margin)

Linear SVM

Non-Linear SVM

Kernel SVM

SVM Regression

 

PCA (Unsupervised Learning)

Dimensionality Reduction

Choosing Number of Dimensions or Principal Components

Incremental PCA

Kernel PCA

When to apply PCA?

Eigen vectors

Eigen values

 

Model Deployment

Pickle (pkl file)

Model load from pkl file and prediction

 

Association Rules

A priori Algorithm

Collaborative Filtering (User-Item based)

Collaborative Filtering (User-User based)

Collaborative Filtering (Item-Item based)

 

Deep Learning:

Introduction to Deep Learning

Tensorflow

Keras

Setting up new environment for Deep Learning

Perceptron model for classification and regression

Perceptron Learning

Limitations of Perceptron model

Multi-layer FF NN model for classification and regression

ML-FF-NN Learning with backpropagation

Applying ML-FF-NN and parameter tuning

Pros and Cons of the Model

 

Image classification

Image Data Preparation

Converting to gray scale

Pixel Value Normalization

Building Pixel Intensity Matrix

Neural Networks

Fully connected Neural Networks

Feed Forward Neural Networks

Convolution Neural Networks

Filters, Max Pooling

Functional APIs

 

Text analytics:

Bag of words

Glove Dictionary

Text Data Preparation

Normalizing Text

Stop word Removal

Whitespace Removal

Stemming

Building Document Term Matrix

NLP (Natural Language Processing)

 

Faq’s

Demo Video’s

Reviews

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Reviews

DATA SCIENCE Rated 4.8 based on 4 reviews.

By: Aadi Shah, Rating:
I enrolled in the Data Science Training program with high expectations, and I must say it exceeded them in every aspect. The course material was carefully planned, and it included a wide range of subjects, including data visualization and big data technologies as well as statistics and machine learning. The lecturers were subject matter specialists who enthusiastically and clearly explained the material.

By: Naveen Choudhary, Rating:
I had great hopes when I participated in the Data Science Training program, the course material was carefully planned, and it included a wide range of subjects, including data visualization and big data technologies as well as statistics and machine learning. The lecturers were subject matter specialists who enthusiastically and clearly explained the material.

By: Gauri Mehta, Rating:
I recently completed the Data Science Training, one of the highlights of this training was the hands-on approach. We worked on real-world projects throughout the course, utilizing innovative tools and technologies that are frequently utilized in the sector. This enabled us to effectively implement the ideas we learnt and create a solid portfolio that demonstrates our abilities to future employers.

By: Arvind, Rating:
I recently completed the Data Science Online Training at BESTWAY Technologies Training Institute, and I'm incredibly pleased with the program. The instructors are not just experts in the field but also excellent educators. They conveyed complex data science concepts with clarity and real-world relevance. This training has not only expanded my knowledge but also boosted my confidence in tackling data science challenges. Thanks to BESTWAY, I feel well-prepared to excel in the field of data science. I highly recommend this program to anyone looking to dive into the world of data-driven insights and analysis.

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