Data Science online training in Hyderabad India
Data science is an interdisciplinary field that uses scientific methods, algorithms, processes, and systems to extract knowledge and insights from structured and unstructured data. It combines elements of mathematics, statistics, computer science, domain expertise, and data visualization to analyze complex datasets, uncover patterns, make predictions, and drive informed decision-making.
SNS Tech Academy offers comprehensive Data Science online training in Hyderabad, India, catering to individuals eager to delve into the dynamic field of data analysis and machine learning. The training program covers a wide spectrum of data science concepts, techniques, and tools, empowering participants to harness the power of data for informed decision-making and predictive modeling. With a curriculum designed by industry experts, students engage in hands-on projects, real-world case studies, and personalized mentorship, gaining practical skills in data wrangling, statistical analysis, machine learning algorithms, and data visualization. Whether aspiring data scientists, analysts, or business professionals seeking to leverage data-driven insights, learners receive the knowledge and certification preparation needed to excel in the competitive landscape of data science. SNS Tech Academy's Data Science online training equips individuals with the expertise to drive innovation, solve complex problems, and unlock new opportunities across diverse industries and domains, fostering a culture of data-driven decision-making and continuous learning.
Data Science Online Training course content :-
Introduction to Data Science
- Need for Data Scientists
- Foundation of Data Science
- What is Business Intelligence
- What is Data Analysis, Data Mining, and Machine Learning
- Analytics vs Data Science
- Value Chain
- Types of Analytics
- Lifecycle Probability
- Analytics Project Lifecycle
- Basis of Data Categorization
- Types of Data
- Data Collection Types
- Forms of Data and Sources
- Data Quality, Changes and Data Quality Issues, Quality Story
- What is Data Architecture
- Components of Data Architecture
- OLTP vs OLAP
- How is Data Stored?
- What is Big Data?
- 5 Vs of Big Data
- Big Data Architecture, Technologies, Challenge and Big Data Requirements
- Big Data Distributed Computing and Complexity
- Hadoop
- Map Reduce Framework
- Hadoop Ecosystem
- What is Data Science?
- Why are Data Scientists in demand?
- What is a Data Product
- The growing need for Data Science
- Large-Scale Analysis Cost vs Storage
- Data Science Skills
- Data Science Use Cases and Data Science Project Life Cycle & Stages
- Map-Reduce Framework
- Hadoop Ecosystem
- Data Acquisition
- Where to source data
- Techniques
- Evaluating input data
- Data formats, Quantity and Data Quality
- Resolution Techniques
- Data Transformation
- File Format Conversions
- Anonymization
- Introduction to R
- Business Analytics
- Analytics concepts
- The importance of R in analytics
- R Language community and eco-system
- Usage of R in industry
- Installing R and other packages
- Perform basic R operations using command line
- Usage of IDE R Studio and various GUI
- The datatypes in R and its uses
- Built-in functions in R
- Subsetting methods
- Summarize data using functions
- Use of functions like head(), tail(), for inspecting data
- Use-cases for problem solving using R
- Various phases of Data Cleaning
- Functions used in Inspection
- Data Cleaning Techniques
- Uses of functions involved
- Use-cases for Data Cleaning using R
- Import data from spreadsheets and text files into R
- Importing data from statistical formats
- Packages installation for database import
- Connecting to RDBMS from R using ODBC and basic SQL queries in R
- Web Scraping
- Other concepts on Data Import Techniques
- What is EDA?
- Why do we need EDA?
- Goals of EDA
- Types of EDA
- Implementing of EDA
- Boxplots, cor() in R
- EDA functions
- Multiple packages in R for data analysis
- Some fancy plots
- Use-cases for EDA using R
- Storytelling with Data
- Principle tenets
- Elements of Data Visualization
- Infographics vs Data Visualization
- Data Visualization & Graphical functions in R
- Plotting Graphs
- Customizing Graphical Parameters to improvise the plots
- Various GUIs
- Spatial Analysis
- Other Visualization concepts
- What is Big Data and Hadoop?
- Challenges of Big Data
- Traditional approach Vs Hadoop
- Hadoop Architecture
- Distributed Model
- Block structure File System
- Technologies supporting Big Data
- Replication
- Fault Tolerance
- Why Hadoop?
- Hadoop Eco-System
- Use cases of Hadoop
- Fundamental Design Principles of Hadoop
- Comparison of Hadoop Vs RDBMS
- Hadoop Cluster and Architecture
- 5 Daemons
- Hands-On Exercise
- Typical Workflow
- Hands-On Exercise
- Writing Files to HDFS
- Hands-On Exercise
- Reading Files from HDFS
- Hands-On Exercise
- Rack Awareness
- Before Map Reduce
- Map Reduce Concepts
- What is Map Reduce?
- Why Map Reduce?
- Map Reduce in real world and Map Reduce Flow
- What is Mapper, Reducer, and Shuffling?
- Word Count Problem
- Hands-On Exercise
- Distributed Word Count Flow and Solution
- Log Processing and Map Reduce
- Hands-On Exercise
- What is Combiner?
- Hands-On Exercise
- What is Partitioner?
- Hands-On Exercise
- What is Counter?
- Hands-On Exercise
- InputFormats/Output Formats
- Hands-On Exercise
- Map Join using MR
- Hands-On Exercise
- Reduce Join using MR
- Hands-On Exercise
- MR Distributed Cache
- Hands-On Exercise
- Using sequence files & images with MR
- Hands-On Exercise
- Planning for Cluster & Hadoop 2.0 Yarn
- Configuration of Hadoop
- Choosing Right Hadoop Hardware and Software?
- Hadoop Log Files?
- Hadoop 1.0 Challenges
- NN Scalability, SPOF, and HA
- Job Tracker Challenges
- Hadoop 2.0 New Features
- Hadoop 2.0 Cluster Architecture & Federation
- Hadoop 2.0 HA
- Yarn & Hadoop Ecosystem
- Yarn MR Application Flow
- Introduction to Pig
- What Is Pig?
- Pig’s Features & Pig Use Cases
- Interacting with Pig
- Basic Data Analysis with Pig
- Hands-On Exercise
- Pig Latin Syntax
- Loading Data
- Hands-On Exercise
- Simple Data Types
- Field Definitions
- Data Output
- Viewing the Schema
- Hands-On Exercise
- Filtering and Sorting Data
- Hands-On Exercise
- Commonly-Used Functions
- Hands-On Exercise: Pig for ETL Processing
- Processing Complex Data with Pig
- Hands-On Exercise
- Storage Formats
- Complex/Nested Data Types
- Hands-On Exercise
- Grouping
- Hands-On Exercise
- Built-in Functions for Complex Data
- Hands-On Exercise
- Iterating Grouped Data
- Hands-On Exercises
- Multi-Dataset Operations with Pig
- Hands-On Exercise
- Techniques for Combining Data Sets
- Joining Data Sets in Pig
- Hands-On Exercise
- Splitting Data Sets
- Hands-On Exercise
- Hive Fundamentals and Architecture
- Loading and Querying Data in Hive
- Hands-On Exercise
- Hive Architecture and Installation
- Comparison with Traditional Database
- HiveQL: Data Types, Operators and Functions
- Hands-On Exercise
- Hive Tables, Managed Tables and External Tables
- Hands-On Exercise
- Partitions and Buckets
- Hands-On Exercise
- Storage Formats, Importing Data, Altering Tables, Dropping Tables
- Hands-On Exercise
- Querying Data, Sorting and Aggregating, Map Reduce Scripts
- Hands-On Exercise
- Joins & Sub queries, Views
- Hands-On Exercise
- Integration, Data manipulation with Hive
- Hands-On Exercise
- User Defined Functions
- Hands-On Exercise
- Appending Data into existing Hive Table
- Hands-On Exercise
- Static partitioning vs dynamic partitioning
- Hands-On Exercise
- CAP Theorem
- HBase Architecture and concepts
- Introduction to HBase
- Client API’s and their features
- HBase tables The ZooKeeper Service
- Data Model, Operations
- Programming and Hands on Exercises
- Introduction to Sqoop
- MySQL Client & server
- Connecting to relational data base using Sqoop
- Importing data using Sqoop from Mysql
- Exporting data using Sqoop to MySql
- Incremental append
- Importing data using Sqoop from Mysql to hive
- Exporting data using Sqoop to MySql from hive
- Importing data using Sqoop from Mysql to hbase
- Using queries and sqoop
- What is Flume?
- Why use Flume, Architecture, configurations
- Master, collector, Agent
- Twitter Data Sentimental Analysis project
- Oozie
- What is Oozie, Architecture, configurations?
- Oozie Job Submission
- Oozie properties
- Hands-on exercises
- Social Media Final Project
- Hadoop Project
- Objective
- Problem Definition
- Solution
- Discuss datasets and specifications of the project
- Hadoop Project in Healthcare
- Objective
- Problem Definition
- Solution
- Discuss datasets and specifications of the project
- Hadoop Project in Banking Domain
- Objective
- Problem Definition
- Solution
- Discuss datasets and specifications of the project
- Introduction to Apache Spark
- Why Spark
- Batch Vs. Real-Time Big Data Analytics
- Batch Analytics – Hadoop Ecosystem Overview
- Real-Time Analytics Options
- Streaming Data – Storm
- In Memory Data – Spark, What is Spark?
- Spark benefits to Professionals
- Limitations of MR in Hadoop
- Components of Spark
- Spark Execution Architecture
- Benefits of Apache Spark
- Hadoop vs Spark
- Features of Scala
- Basic Data Types of Scala
- Val vs Var
- Type Inference
- REPL
- Objects & Classes in Scala
- Functions as Objects in Scala
- Anonymous Functions in Scala
- Higher Order Functions
- Lists in Scala
- Maps
- Pattern Matching
- Traits in Scala
- Collections in Scala
- Spark & Distributed Systems
- Spark for Scalable Systems
- Spark Execution Context
- What is RDD
- RDD Deep Dive and Dependencies
- RDD Lineage
- Spark Application In Depth and Spark Deployment
- Parallelism in Spark
- Caching in Spark
- Spark Transformations, Actions, Cluster and SQL Introduction
- Spark Data Frames
- Spark SQL with CSV, JSON, and Database
- Features of Spark Streaming
- Micro Batch
- Dstreams
- Transformations on Dstreams
- Spark Streaming Use Case
- Descriptive Statistics
- Central Tendency Measures
- The Story of Average
- Dispersion Measures
- Data Distributions
- Central Limit Theorem
- What is Sampling
- Why Sampling
- Sampling Methods
- Inferential Statistics
- What is Hypothesis testing
- Confidence Level
- Degrees of freedom
- what is pValue
- Chi-Square test
- What is ANOVA
- Correlation vs Regression
- Uses of Correlation and Regression
- ML Fundamentals
- ML Common Use Cases
- Understanding Supervised and Unsupervised Learning Techniques
- Clustering
- Similarity Metrics
- Distance Measure Types: Euclidean, Cosine Measures
- Creating predictive models
- Understanding K-Means Clustering
- Understanding TF-IDF, Cosine Similarity and their application to Vector Space Model
- Case study
- Implementing Association rule mining
- Case study
- Understanding Process flow of Supervised Learning Techniques
- Decision Tree Classifier
- How to build Decision trees
- Case study
- Random Forest Classifier
- What is Random Forests
- Features of Random Forest
- Out of Box Error Estimate and Variable Importance
- Case study
- Naive Bayes Classifier
- Case study
- Project Discussion
- Problem Statement and Analysis
- Various approaches to solving a Data Science Problem
- Pros and Cons of different approaches and algorithms
- Linear Regression
- Case study
- Logistic Regression
- Case study
- Text Mining
- Case study
- Sentimental Analysis
- Case study
- Python Overview
- About Interpreted Languages
- Advantages/Disadvantages of Python pydoc
- Starting Python
- Interpreter PATH
- Using the Interpreter
- Running a Python Script
- Python Scripts on UNIX/Windows, Editors and IDEs
- Using Variables
- Keywords
- Built-in Functions
- StringsDifferent Literals
- Math Operators and Expressions
- Writing to the Screen
- String Formatting
- Command Line Parameters and Flow Control
- Lists
- Tuples
- Indexing and Slicing
- Iterating through a Sequence
- Functions for all Sequences
- Using Enumerate()
- Operators and Keywords for Sequences
- The xrange() function
- List Comprehensions
- Generator Expressions
- Dictionaries and Sets
- Functions
- Function Parameters
- Global Variables
- Variable Scope and Returning Values. Sorting
- Alternate Keys
- Lambda Functions
- Sorting Collections of Collections, Dictionaries and Lists in Place
- Errors and Exception Handling
- Handling Multiple Exceptions
- The Standard Exception Hierarchy
- Using Modules
- The Import Statement
- Module Search Path
- Package Installation Ways
- The Sys Module
- Interpreter Information
- STDIO
- Launching External Programs
- path directories and Filenames
- Walking Directory Trees
- Math Function
- Random Numbers
- Dates and Times
- Zipped Archives
- Introduction to Python Classes
- Defining Classes
- Initializers
- Instance Methods
- Properties
- Class Methods and Data Static Methods
- Private Methods and Inheritance
- Module Aliases and Regular Expressions
- Debugging
- Dealing with Errors
- Using Unit Tests
- Project Skeleton
- Required Packages
- Creating the Skeleton
- Project Directory
- Final Directory Structure
- Testing your Setup
- Using the Skeleton
- Creating a Database with SQLite 3
- CRUD Operations
- Creating a Database Object.
- Introduction to Machine Learning
- Areas of Implementation of Machine Learning
- Why Python
- Major Classes of Learning Algorithms
- Supervised vs Unsupervised Learning
- Learning NumPy
- Learning Scipy
- Basic plotting using Matplotlib
- Machine Learning application
- Classification Problem
- Classifying with k-Nearest Neighbours (kNN)
- General Approach to kNN
- Building the Classifier from Scratch
- Testing the Classifier
- Measuring the Performance of the Classifier
- Clustering Problem
- What is K-Means Clustering
- Clustering with k-Means in Python and an
- Introduction to Pandas
- Creating Data Frames
- GroupingSorting
- Plotting Data
- Creating Functions
- Converting Different Formats
- Combining Data from Various Formats
- Slicing/Dicing Operations.
- Introduction to Scikit-Learn
- Inbuilt Algorithms for Use
- What is Hadoop and why it is popular
- Distributed Computation and Functional Programming
- Understanding MapReduce Framework Sample MapReduce Job Run
- PIG and HIVE Basics
- Streaming Feature in Hadoop
- Map Reduce Job Run using Python
- Writing a PIG UDF in Python
- Writing a HIVE UDF in Python
- Pydoop and MRjob Basics
- Real world project
Data Science is an interdisciplinary field that utilizes scientific methods, algorithms, and systems to extract knowledge and insights from structured and unstructured data. While traditional statistics primarily focuses on hypothesis testing and inference from sample data, Data Science encompasses a broader range of techniques, including machine learning, data mining, and predictive modeling, to analyze large datasets and drive decision-making.
The stages of a Data Science project lifecycle typically include problem definition, data collection and preprocessing, exploratory data analysis (EDA), feature engineering, model building, model evaluation, and deployment. Each stage involves specific tasks and techniques aimed at understanding the data, developing predictive models, and deploying solutions to address business problems.
Supervised learning involves training a model on labeled data, where the desired output is provided along with the input features. The model learns to make predictions based on this labeled data. In contrast, unsupervised learning involves training a model on unlabeled data, where the algorithm learns patterns and relationships within the data without explicit guidance on the output.
Overfitting occurs when a model learns the training data too well, capturing noise or random fluctuations that are specific to the training dataset and do not generalize to new data. To prevent overfitting, techniques such as cross-validation, regularization, and feature selection can be used. These methods help to ensure that the model learns meaningful patterns from the data without memorizing noise.
Precision measures the proportion of true positive predictions among all positive predictions made by a classifier. It quantifies the accuracy of positive predictions. Recall, on the other hand, measures the proportion of true positive predictions among all actual positive instances in the dataset. It quantifies the completeness of positive predictions made by a classifier.
Feature engineering is the process of selecting, transforming, and creating new features from raw data to improve the performance of machine learning models. It involves techniques such as encoding categorical variables, scaling numerical features, handling missing values, and creating interaction terms. Feature engineering is essential because the quality of features directly impacts the effectiveness of predictive models.
Cross-validation is a technique used to assess the performance of machine learning models by splitting the dataset into multiple subsets, training the model on a portion of the data, and evaluating its performance on the remaining data. It helps to provide a more accurate estimate of a model's performance by reducing the variance introduced by a single train-test split.
Regularization is a technique used to prevent overfitting in machine learning models by adding a penalty term to the model's cost function. This penalty discourages the model from learning overly complex patterns that may not generalize well to new data. Common regularization techniques include L1 regularization (Lasso), L2 regularization (Ridge), and ElasticNet, which balance between L1 and L2 penalties.
The curse of dimensionality refers to the phenomena where the performance of machine learning algorithms deteriorates as the number of features (dimensions) in the dataset increases. As the dimensionality increases, the volume of the feature space grows exponentially, leading to sparsity and increased computational complexity. This can cause issues such as overfitting, increased model complexity, and decreased generalization performance.
Common algorithms used in Data Science include linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-nearest neighbors (KNN), naive Bayes, clustering algorithms (k-means, hierarchical clustering), and dimensionality reduction techniques (PCA, t-SNE). Each algorithm has its strengths and weaknesses and is suitable for different types of problems and data.
