Data Science
Data Science is an interdisciplinary field that uses techniques from statistics, computer science, and domain knowledge to extract insights and knowledge from structured and unstructured data. It involves processes like data collection, cleaning, analysis, visualization, and modeling to support decision-making, predictions, and automation. Common tools include Python, R, SQL, and machine learning libraries.
Data Science is an art of making data driven decisions. To make that data driven decision it uses scientific methods, processes, algorithms to extract knowledge and insights from data. It is used for examining, cleaning, manipulating, transforming and generating information from the data. Now a days in Business world data analytics plays a vital role to form decisions more scientifically and help to increase operational efficiency. We provide one of the best Data Science online Course, Training & Certification.
What we will learn
- Connection, Navigation, Data Sources
- Data Extraction, Data Prep
- Data Pre-Processing
- Exploratory Data Analytics
- Data Wrangling
- Data Cleaning
- Data Munging
- Statistics For Business Analytics
- Data Analytics
- Machine Learning
- Data Visualization
- Python for everyone
Top skill in demand now a days is to process raw data into business insights. There is no special programming language dedicated to data science but looking at the exciting features of the python language you can make your mind. Python has great features like fast and high computational capability, extremely compatible, cross platform support , distributed computing and vector arithmetic.
In this course we will learn python programming, statistics and analytics used for business analytics. We will learn data wrangling, data cleansing as well as data visualization using popular Python libraries like Numpy, Pandas, Matplotlib, and seaborn. Â In this course you will get to learn apply exploratory data analytics the essential part of data analytics.
By the end of this you will be able to extract, read and write data from csv files, data cleansing, data manipulation, data visualization, run inferential statistics, understand the business problems, based on problems you will be able to select and apply machine learning models and deploy it.
Who can go for this
Data Science is meant for all and everyone should go for this, learn to play with data and grasping required skills isn’t just valuable, its essential now. Does not matter from which field you – economics, computer science, chemical, electrical, are statistics, mathematics, operations you will have to learn this.
Advantages:
Countless Batch Access
Indusrty Expert Trainers
Shareable Certificate
Learn From Anywhere
Career Transition Guidance
Real-Time Project
Industry Experts
Interview Preparation
Class Recordings
- 5 Sections
- 171 Lessons
- 10 Weeks
- Advance Excel18
- 1.1Introduction to Excel for Analytics Overview20 Minutes
- 1.2Importance of Excel in Data Analysis60 Minutes
- 1.3Excel Interface and Basic Navigation
- 1.4Essential Excel Functions For Data Analysis
- 1.5Understanding and using basic functions (SUM, AVERAGE, COUNT)
- 1.6Working with Mathematical and Statistical Function
- 1.7Text Functions for Data Cleaning and Manipulation
- 1.8Logical Functions (IF, AND, OR)
- 1.9Data Import and Cleaning in Excel Importing Data from Various
- 1.10Importing Data From Various Sources (CSV, Excel, Databases)
- 1.11Data Cleaning techniques and best practices Handling Missing Data
- 1.12Data Visualization in Excel Creating Basic Charts (Bar Charts, Line Charts, Pie Charts)
- 1.13Introduction to Pivot Tables
- 1.14Pivot Tables and Pivot Charts
- 1.15Creating Pivot Tables for Data Summarization
- 1.16Building Pivot Charts for Visual Analysis
- 1.17Data Analysis with Excel Tables
- 1.18Introduction to Excel Tables
- SQL34
- 2.1Introduction to Databases and SQL60 Minutes
- 2.2Understanding Databases60 Minutes
- 2.3Definition and Types of Databases
- 2.4Relational Databases and Non-Relational Databases
- 2.5Introduction to SQL and What is SQL?
- 2.6SQL history and evolution
- 2.7Importance of SQL in the industry
- 2.8SQL Basic Commands
- 2.9Filtering Data using WHERE Clause
- 2.10Retrieving Data From a single table
- 2.11Advanced Querying
- 2.12Ata Modification
- 2.13COMMIT and ROLL BACK Statements
- 2.14Constraints (PRIMARY KEY, FOREIGN KEY, UNIQUE, CHECK)
- 2.15Aggregate Functions (SUM, AVG, COUNT, MIN, MAX)
- 2.16Stored Procedures and Functions
- 2.17Creating Stored Procedures
- 2.18Input and Output Parameters
- 2.19User-Defined Functions
- 2.20Database Security
- 2.21User Roles and Permissions
- 2.22GRANT and REVOKE Statements
- 2.23Securing Sensitive Data
- 2.24Introduction to NoSQL Databases
- 2.25Best Practices and Optimization
- 2.26Writing Efficient Queries
- 2.27Query Optimization Techniques
- 2.28Case Studies and Real-World Applications
- 2.29Analyzing Real-World Scenario
- 2.30Building Practical Solutions with SQL
- 2.31Views and Indexes
- 2.32Creating and Managing Views
- 2.33Indexes and their impact on performance
- 2.34Data Filtering and Aggregation
- Power BI37
- 3.1Overview30 Minutes
- 3.2Power BI ecosystem and components20 Minutes
- 3.3Getting Started with Power BI Desktop30 Minutes
- 3.4Power BI service overview25 Minutes
- 3.5Connecting to different data sources (Excel, Databases, Cloud Services)
- 3.6Loading and transforming data using Power Query Editor
- 3.7Data Modeling and Relationship
- 3.8Building and Designing Reports
- 3.9Creating Visuals (Tables, Charts, Maps, etc.)
- 3.10Formatting and customizing visuals
- 3.11Using Themes and Templates
- 3.12DAX (Data Analysis Expression) Fundamentals
- 3.13Creating Calculated columns and measures
- 3.14The intelligence Function
- 3.15Hierarchies and drill-downs
- 3.16Publishing reports to Power BI Services
- 3.17Creating and sharing dashboards
- 3.18Setting up and managing workspaces
- 3.19Creating and using Quick Insights
- 3.20Analyzing data trends and patterns
- 3.21Building advanced analysis with AI visuals
- 3.22Exploring and Interpreting data insights
- 3.23Integrating Power BI with Excel
- 3.24Embedding Power BI reports in other applications
- 3.25Using Power BI REST API for automations
- 3.26Power BI and Azure integration
- 3.27Power BI Gateway setup and configuration
- 3.28Managing data refresh schedules
- 3.29Implementing Row-level security
- 3.30Monitoring and managing Power BI workspaces
- 3.31Performance Optimization techniques
- 3.32Designing efficient data models
- 3.33Report and Dashboard Optimization
- 3.34Best practices for Data visualization
- 3.35Overview of Power BI Certification
- 3.36Exam Preparation tips and resources
- 3.37Continuing learning paths and resources
- Python64
- 4.1Overview
- 4.2The Python Ecosystem
- 4.3Why python over R/SAS
- 4.4What do expect after you learn Python
- 4.5Understanding and choosing between different Python Versions
- 4.6Setting up python on any machine (Windows/Linux/Mac)
- 4.7Using Anaconda, the Python distribution
- 4.8Exploring the different third-party IDEs (PyCharm, Spyder, Jupyter, Sublime)
- 4.9Setting up a suitable Workspace
- 4.10Running the first Python program
- 4.11Python Syntax
- 4.12Interactive Mode/Script Mode Programming
- 4.13Identifiers and Keywords
- 4.14Single and Multi-line Comments
- 4.15Data Types in Python (Numbers, String, List, Tuple, Set, Dictionary)
- 4.16Implicit and Explicit Conversions
- 4.17Understanding Operators in Python
- 4.18Working with various Date and Time formats
- 4.19Working with Numeric data types – int, long, float, complex
- 4.20String Handling, Escape Characters, String Operations
- 4.21Working with Unicode Strings
- 4.22Local and Global Variables
- 4.23Flow Control and Decision Making in Python
- 4.24Understanding if else conditional statement
- 4.25Nested Condition
- 4.26Working in Iterations
- 4.27Understanding the for and while Loop
- 4.28Nested Loops
- 4.29Loops Control Statements – breaks, continue, Pass
- 4.30Understanding Dictionary – The Key value pairs
- 4.31List Comprehensions and Dictionary Comprehensions
- 4.32Functions, Arguments, Returns Statements
- 4.33Packages, Libraries and Modules
- 4.34Error Handling in Python
- 4.35Reading data from files (TXT, CSV, Excel, JSON, KML, etc.)
- 4.36Writing data to desired file format
- 4.37Creating connections to Databases
- 4.38Importing/Exporting data from/to NoSQL databases (MongoDB)
- 4.39Importing/Exporting data from/to RDBMS (PostgreSQL)
- 4.40Getting data from website
- 4.41Manipulating Configuration files
- 4.42Introduction to Data Wrangling Techniques
- 4.43Why is transformation so important
- 4.44Understanding Database Architecture – (RDBMS, NoSQL Databases)
- 4.45Understanding the strength/limitations of each complex data containers
- 4.46Understanding Sorting, Filtering, Redundancy, Cardinality
- 4.47Sampling, Aggregations
- 4.48Converting from one Data Type to another
- 4.49Introduction to Numpy and its superior capabilities
- 4.50Understanding differences between Lists and Arrays
- 4.51Understanding Vectors and Matrices, Dot Products and Matrix Products
- 4.52Universal Array Functions
- 4.53Understanding Pandas and its architecture
- 4.54Getting to know Series and Data Frames, Columns and Indexes
- 4.55Getting Summary Statistics of the Data
- 4.56Data Alignment, Ranking and Sorting
- 4.57Combining/Splitting Data Frames, Reshaping, Grouping
- 4.58Identifying Outliers and performing Binning Tasks
- 4.59Cross Tabulation, Permutations, the apply() functions
- 4.60Introduction to Data Visualization
- 4.61Line Chart, Scatter plots, Box plots, Violin plots
- 4.62Understanding Probability Distribution
- 4.63Histograms, Heat maps and Clustered Matrices
- 4.64Plotting Kernel Density Estimate plots
- Deep Leaning18
- 5.1Logistic Regressions
- 5.2K-Nearest Neighbours (K-NN)
- 5.3Support Vector Machines
- 5.4Kernel SVM
- 5.5Naive Bayes Classifier
- 5.6Decision Tree Classification
- 5.7Random Forest Classification
- 5.8Clustering – Intuition
- 5.9K-Means Clustering
- 5.10Hierarchical Clustering
- 5.11Principal Component Analysis (PCA)
- 5.12Linear Discriminant Analysis (LDA)
- 5.13Understanding Kernel PCA
- 5.14Understanding the need for model selection
- 5.15What is Overlifting
- 5.16Understanding Bias Variance Trade-Off
- 5.17K-fold cross validation
- 5.18Understanding and applying Grid Search
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