Data Science Training in Gandhinagar

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Data Science Online-Offline Virtual Live Learning in Gandhinagar

Data Science Market in india will be worth 11 millions jobs by 2026 and the Data Analytics Outsourcing market in India is worth $26 Billions. India is second to the United States in terms of the number of job openings in Data Science. In 2019, 93,500 positions in india by august 2021 data science and analytics were vacant due to the lack of qualified candidates. The top sectors creating the most Data Science jobs are BFSI, Energy, Pharmaceutical, HealthCare, E-commerce, Media, and Retail. Today large companies, medium-sized companies and even startups are willing to recruit the data scientists in India. The main skills are Big Data, Software and User Testing, Mobile Development, Cloud Computing, and Software Engineering Management. The short supply has led to a spike in salaries. Data science professionals with 2-10 years of experience get annual salaries in the range of 10 lakh to 85 lakh, while more experienced people can command annual salaries upwards of 1.6 crore, according to the Talent Trends Report 2021 by Michael Page India, a recruitment Consultants. Experts with more than 15 years of experience can get paid up to 2 crore per year.

Course Overview

All industries now utilize data and Data-Science and Data-Analytics are increasingly identified as key industrial activities. The position of Data Scientist is rapidly becoming a required post for any company that wishes to take full advantage of the data that they collect. This course is designed to give you the skills to step into a career as a Data Scientist in a wide range of industries and companies.

Course Overview

You should take this course if you want to become a Data Scientist or if you want to learn about
the field.
This course is for you if you want a great career.
The course is also ideal for beginners, as it starts from the fundamentals and gradually builds up
your skills.

Prerequisites

No Prerequisites required.

What You Learn

Statistical analysis, Python programming with NumPy, pandas, matplotlib, and
Seaborn, Advanced statistical analysis, Machine Learning with stats models and
scikit-learn, Deep learning with TensorFlow.
Understand the mathematics behind Machine Learning.
Learn how to pre-process data.
Start coding in Python and learn how to use it for statistical analysis.
Be able to create Machine Learning algorithms in Python, using NumPy and scikit-
learn.
Improve Machine Learning algorithms by studying under fitting, over fitting, training,
validation, n-fold cross validation, testing, and how hyper parameters could improve
performance.
Unfold the power of deep neural networks.

COURSE DURATION: 75 hours

Data Science Syllabus Overview

Foundations

Python Programming and Computer Science
Types, Flow Control and Data Structures

SciPy Stack
NumPy, Pandas and matplotlib

Mathematics
Statistics, Probability and Linear Algebra

Data Analysis

Getting, cleaning, analyzing and visualizing raw data is the main responsibility of the industry data scientists.

Statistical Inference
Probability, Distributions and Hypothesis Testing

Summarizing and Visualizing Data
Descriptive Statistics, Univariate and Multivariate Exploratory Data Analysis

Machine Learning

Students will learn how to explore new data sets, implement a comprehensive set of machine learning algorithms from scratch and master all the components of a predictive model such as data preprocessing, feature engineering, model selection, performance metrics and hyperparameter optimization.

Predictive Modeling
Regression, Classification, Data Preprocessing, Model Evaluation and Ensembles.

Data Mining
Dimensionality Reduction, Clustering, Association Rules.

Specialty Topics
Data Engineering, Natural Language Processing and Neural Networks.

Program Curriculum in Detail

Module 1: Fundamentals of Python

Working with Numerical Data
Rescaling a Feature
Standardizing a Feature
Normalizing Observations
Generating Polynomial and Interaction Features
Transforming Features
Detecting Outliers
Handling Outliers
Discretization of Features
Grouping Observations Using Clustering
Deleting Observations with Missing Values
Imputing Missing Values

Working with Categorical Data
Encoding Nominal Categorical Features
Encoding Ordinal Categorical Features
Encoding Dictionaries of Features
Imputing Missing Class Values
Handling Imbalanced Classes

Working with Text
Introduction
Cleaning Text
Parsing and Cleaning HTML
Removing Punctuation
Tokenizing Text
Removing Stop Words
Stemming Words
Tagging Parts of Speech
Encoding Text as a Bag of Words
Weighting Word Importance

Working with Images
Loading Images
Saving Images
Resizing Images
Cropping Images
Blurring Images
Sharpening Images
Enhancing Contrast
Isolating Colors
Binarizing Images
Removing Backgrounds
Detecting Edges
Detecting Corners
Creating Features for Machine Learning
Encoding Mean Color as a Feature
Encoding Color Histograms as Features

Visual Aids for EDA
Line chart
Steps involved
Bar charts
Scatter plot
Bubble chart
Scatter plot using seaborn
Area plot and stacked plot
Pie chart
Table chart
Polar chart

Histogram
Lollipop chart
Choosing the best chart
Other libraries to explore

Descriptive Statistics
Understanding statistics
Distribution function
Uniform distribution
Normal distribution
Exponential distribution
Binomial distribution
Cumulative distribution function
Descriptive statistics
Measures of central tendency
Mean/average
Median
Mode
Measures of dispersion
Standard deviation
Variance
Skewness
Kurtosis
Types of kurtosis
Calculating percentiles
Quartiles
Visualizing quartiles

Correlation
Introducing correlation
Types of analysis
Understanding univariate analysis
Understanding bivariate analysis
Understanding multivariate analysis

Hypothesis Testing
Hypothesis testing principle
Types of hypothesis testing
T-test

Module 1: Fundamentals of Python

Vectors, Matrices, and Arrays
Creating a Vector
Creating a Matrix
Creating a Sparse Matrix
Selecting Elements
Describing a Matrix
Applying Operations to Elements
Finding the Maximum and Minimum Values
Calculating the Average, Variance, and Standard Deviation
Reshaping Arrays
Transposing a Vector or Matrix
Flattening a Matrix
Finding the Rank of a Matrix
Calculating the Determinant
Getting the Diagonal of a Matrix
Calculating the Trace of a Matrix
Finding Eigenvalues and Eigenvectors
Calculating Dot Products
Adding and Subtracting Matrices
Multiplying Matrices
Inverting a Matrix
Generating Random Values

Loading Data
Loading a Sample Dataset
Creating a Simulated Dataset
Loading a CSV File
Loading an Excel File
Loading a JSON File
Querying a SQL Database

Data Wrangling
Creating a Data Frame
Describing the Data
Navigating Data Frames

Selecting Rows Based on Conditionals
Replacing Values
Renaming Columns
Finding the Minimum, Maximum, Sum, Average, and Count
Finding Unique Values
Handling Missing Values
Deleting a Column
Deleting a Row
Dropping Duplicate Rows
Grouping Rows by Values
Grouping Rows by Time
Looping Over a Column
Applying a Function Over All Elements in a Column
Applying a Function to Groups
Concatenating Data Frames
Merging Data Frames

Module 2: Fundamentals of Exploratory Data Analysis

Loading Data
Loading a Sample Dataset
Creating a Simulated Dataset
Loading a CSV File
Loading an Excel File
Loading a JSON File
Querying a SQL Database

Data Wrangling
Creating a Data Frame
Describing the Data
Navigating Data Frames

Module 3: Introduction to Data Science in Python

Application of Data Science
What is Machine Learning
Supervised Learning
Un Supervised Learning
Reinforcement Learning

Module 4: Machine Learning Algorithms

Supervised Learning
Linear Regression
Fitting a Line
Handling Interactive Effects
Fitting a Nonlinear Relationship
Reducing Variance with Regularization
Reducing Features with Lasso Regression

Trees and Forests
Training a Decision Tree Classifier
Training a Decision Tree Regressor
Visualizing a Decision Tree Model
Training a Random Forest Classifier
Training a Random Forest Regressor
Identifying Important Features in Random Forests
Selecting Important Features in Random Forests
Handling Imbalanced Classes
Controlling Tree Size
Improving Performance Through Boosting
Evaluating Random Forests with Out-of-Bag Errors

K-Nearest Neighbors
Finding an Observation’s Nearest Neighbors
Creating a K-Nearest Neighbor Classifier
Identifying the Best Neighborhood Size
Creating a Radius-Based Nearest Neighbor Classifier

Logistic Regression
Training a Binary Classifier
Training a Multiclass Classifier
Reducing Variance Through Regularization
Training a Classifier on Very Large Data
Handling Imbalanced Classes

Support Vector Machines
Training a Linear Classifier
Handling Linearly Inseparable Classes Using Kernels
Creating Predicted Probabilities
Identifying Support Vectors
Handling Imbalanced Classes

Naive Bayes
Training a Classifier for Continuous Features
Training a Classifier for Discrete and Count Features
Training a Naive Bayes Classifier for Binary Features
Calibrating Predicted Probabilities

Unsupervised Learning
Clustering
Clustering Using K-Means
Speeding Up K-Means Clustering
Clustering Using Meanshift
Clustering Using DBSCAN
Clustering Using Hierarchical Merging

Module 5 : Deep Learning

Neural Networks
Preprocessing Data for Neural Networks
Designing a Neural Network
Training a Binary Classifier
Training a Multiclass Classifier
Training a Regressor
Making Predictions
Visualize Training History
Reducing Overfitting with Weight Regularization
Reducing Overfitting with Early Stopping
Reducing Overfitting with Dropout
Saving Model Training Progress
k-Fold Cross-Validating Neural Networks
Tuning Neural Networks
Visualizing Neural Networks
Classifying Images
Improving Performance with Image Augmentation
Classifying Text

Saving and Loading Trained Models
Introduction
Saving and Loading a scikit-learn Model
Saving and Loading a Keras Model

Natural Language Processing
Developing Text Classifiers
Building Pipelines for NLP Projects

Two live projects