# Data Science Training in Farrukhabad

TMS Data science has introduced the most ample Data Science Training in **Farrukhabad**.☎+91-7569649640, Machine Learning |Python R Language Programming| Deep Learning |Java,.Net,Mean Full Stack Web Development | AWS- Devops | MSBI | Power BI |Testing tools | Tableau Hadoop Big Data, Azure, Azure Data Engineer, Azure Data Factory, Azure Data Bricks,Data Analytics Course Training Institute in Farrukhabad India

# Data Science Online-Offline Virtual Live Learning in Farrukhabad

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

### 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

**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

**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 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**