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Course content

About Post Graduate Certification Program in Data Science & AI in Hughes Springs

  • Define Python
  • Overview of Python
  • Understand why Python is Popular
  • Setup Python Environment
  • Understand Operands and Expressions
  • The Companies using Python
  • Different Applications where Python is Used
  • Discuss Python Scripts on UNIX/Windows
  • Values, Types, Variables
  • Operands and Expressions
  • Conditional Statements
  • Loops
  • Command Line Arguments
  • Writing to the Screen
  • Write your First Python Program
  • Understand Command Line Parameters and Flow Control
  • Python files I/O Functions
  • Numbers
  • Strings and related operations
  • Tuple: properties, related operations, compared with list
  • List: properties, related operations
  • Dictionary: properties, related operations
  • Set: properties
  • Understand Python Standard Libraries
  • Packages and Modules: Modules, Import Options, sys.path
  • Functions: Syntax, Arguments, Keyword Arguments, Return Values
  • Function Parameters
  • Global Variables
  • Variable Scope and Returning Values
  • Lambda: Features, Syntax, Options, Compared with Functions
  • Sorting: Sequences, Dictionaries, Limitations of Sorting
  • Errors and Exceptions: Types of Issues, Remediation
  • Object-Oriented Concepts
  • Modules Used in Python
  • The Import Statements
  • Module Search Path
  • Package Installation Methods
  • Errors and Exception Handling
  • Handling Multiple Exceptions
  • Create arrays using NumPy
  • Perform various operations on arrays and manipulate them
  • Indexing, slicing, and iterating
  • Read and write data from text/CSV files into arrays and vice-versa
  • Create Series and DataFrames in Pandas
  • Data structures and index operations in Pandas
  • Importing and exporting data
  • Indexing and slicing of data structures in Pandas
  • Reading and writing data from Excel/CSV formats into Pandas
  • Create simple plots in Matplotlib
  • Grids, axes, plots, markers, colors, fonts, and styling
  • Types of plots: bar graphs, pie charts, histograms, contour plots
  • Choose the right plot format for a problem at hand judiciously
  • Scale and add style to your plots
  • Basic functionalities of a data object
  • Merging of data objects
  • Concatenation of data objects
  • Types of joins on data objects
  • Exploring a dataset
  • Analyzing a dataset
  • Introduction to Excel
  • Basic Excel functions and formulas
  • Data entry and formatting
  • Data organization
  • Charts and graphs
  • Basic data analysis
  • Working with multiple worksheets
  • Basic functions for financial calculations
  • Overview of BI concepts
  • Why we need BI
  • Introduction to SSBI
  • SSBI tools
  • Why Power BI
  • What is Power BI
  • Building blocks of Power BI
  • Getting started with Power BI Desktop
  • Getting Power BI tools
  • Introduction to tools and terminology
  • Dashboard in minutes
  • Refreshing Power BI Service data
  • Interacting with your dashboards
  • Sharing dashboards and reports
  • Power BI Desktop
  • Extracting data from various sources
  • Workspaces in Power BI
  • Data Transformation
  • Measures and Calculated Columns
  • Query Editor in Power BI
  • Introduction to Modelling
  • Modelling Data
  • Manage Data Relationships
  • Optimize Data Models
  • Cardinality and Cross Filtering
  • Default Summarization & Sort By
  • Creating Calculated Columns
  • Creating Measures & Quick Measures
  • What is DAX?
  • Data Types in DAX
  • Calculation Types
  • Syntax, Functions, Context Options
  • DAX Functions
  • Time Intelligence
  • Information
  • Logical
  • Mathematical
  • Statistical
  • Text and Aggregate
  • Measures in DAX
  • ROW Context and Filter Context in DAX
  • Operators in DAX - Real-time Usage
  • Quick Measures in DAX - Auto validations
  • PowerPivot xVelocity & Vertipaq Store
  • In-Memory Processing: DAX Performance
  • Introduction to Modelling
  • Optimize Data Models
  • Setup and Manage Relationships
  • Cardinality and Cross Filtering
  • Default Summarization & Sort by
  • Creating Calculated Columns
  • Creating Measures & Quick Measures
  • How to use Visual in Power BI?
  • What Are Custom Visuals?
  • Creating Visualisations and Colour Formatting
  • Setting Sort Order
  • Scatter & Bubble Charts & Play Axis
  • Tooltips and Slicers, Timeline Slicers & Sync Slicers
  • Cross Filtering and Highlighting
  • Visual, Page, and Report Level Filters
  • Drill Down/Up
  • Hierarchies and Reference/Constant Lines
  • Tables, Matrices & Conditional Formatting
  • KPI's, Cards & Gauges
  • Map Visualizations
  • Custom Visuals
  • Managing and Arranging
  • Drill through and Custom Report Themes
  • Why Dashboard? and Dashboard vs Reports
  • Creating Dashboards
  • Configuring a Dashboard: Dashboard Tiles, Pinning Tiles
  • Power BI Q&A
  • Quick Insights in Power BI
  • Power BI embedded and REST API
  • Custom Data Gateways
  • Exploring live connections to data with Power BI
  • Connecting directly to SQL Azure, HD Spark, and SQL Server Analysis Services/MySQL
  • Introduction to Power BI Development API
  • Excel with Power BI: Connect Excel to Power BI, Power BI Publisher for Excel
  • Content packs
  • Update content packs
  • Extracting data out of Azure SQL using R
  • Using R to call the Azure ML web service and send it the unscored data
  • Writing the output of the Azure ML model back into SQL
  • Reading scored data into Power BI using R
  • Publishing the Power BI file to the Power BI service
  • Scheduling a refresh of the data using the Personal Gateway
  • Introduction and Sharing Options Overview
  • Publish from Power BI Desktop and Publish to Web
  • Share Dashboard with Power BI Service
  • Workspaces and Apps (Power BI Pro) and Content Packs (Power BI Pro)
  • Print or Save as PDF and Row Level Security (Power BI Pro)
  • Export Data from a Visualization and Publishing for Mobile Apps
  • Export to PowerPoint and Sharing Options Summary
  • What is data visualization?
  • Comparison and benefits against reading raw numbers
  • Real use cases from various business domains
  • Some quick and powerful examples using Tableau without going into the technical details of Tableau
  • Installing Tableau
  • Tableau interface
  • Connecting to Data Source
  • Tableau data types
  • Data preparation
  • Installation of Tableau Desktop
  • Architecture of Tableau
  • Interface of Tableau (Layout, Toolbars, Data Pane, Analytics Pane, etc.)
  • How to start with Tableau
  • The ways to share and export the work done in Tableau
  • Connection to Excel
  • Cubes and PDFs
  • Management of metadata and extracts
  • Data preparation
  • Joins (Left, Right, Inner, and Outer) and Union
  • Dealing with NULL values
  • Cross-database joining
  • Data extraction
  • Data blending
  • Refresh extraction
  • Incremental extraction
  • How to build extract
  • Mark, highlight, sort, group, and use sets
  • Constant sets
  • Computed sets
  • Bins
  • Filters (addition and removal)
  • Filtering continuous dates, dimensions, and measures
  • Interactive filters, marks card, and hierarchies
  • Creating folders in Tableau
  • Sorting in Tableau
  • Types of sorting
  • Filtering in Tableau
  • Types of filters
  • Filtering the order of operation
  • Using Formatting Pane to work with menu, fonts, alignments, settings, and copy-paste
  • Formatting data using labels and tooltips
  • Editing axes and annotations
  • K-means cluster analysis
  • Trend and reference lines
  • Visual analytics in Tableau
  • Forecasting, confidence intervals, reference lines, and bands
  • Working on coordinate points
  • Plotting longitude and latitude
  • Editing unrecognized locations
  • Customizing geocoding, polygon maps, WMS (web mapping services)
  • Working on the background image, including adding images
  • Plotting points on images and generating coordinates from them
  • Map visualization, custom territories, Mapbox, WMS map
  • How to create map projects in Tableau
  • Creating dual axes maps and editing locations
  • Calculation syntax and functions in Tableau
  • Various types of calculations, including Table, String, Date, Aggregate, Logic, and Number
  • LOD (Level of Detail) expressions, including concept and syntax
  • Aggregation and replication with LOD expressions
  • Nested LOD expressions
  • Levels of detail: fixed level, lower level, and higher level
  • Quick table calculations
  • The creation of calculated fields
  • Predefined calculations
  • How to validate calculations
  • Creating parameters
  • Parameters in calculations
  • Using parameters with filters
  • Column selection parameters
  • Chart selection parameters
  • How to use parameters in the filter session
  • How to use parameters in calculated fields
  • How to use parameters in the reference line
  • Dual axes graphs
  • Histograms
  • Single and dual axes
  • Box plot
  • Charts: motion, Pareto, funnel, pie, bar, line, bubble, bullet, scatter, and waterfall charts
  • Maps: tree and heat maps
  • Market basket analysis (MBA)
  • Using Show me
  • Text table and highlighted table
  • Building and formatting a dashboard using size, objects, views, filters, and legends
  • Best practices for making creative as well as interactive dashboards
  • Using the actions
  • Creating stories, including the introduction of story points
  • Creating and updating story points
  • Adding catchy visuals in stories
  • Adding annotations with descriptions; dashboards and stories
  • What is a dashboard?
  • Highlight actions, URL actions, and filter actions
  • Selecting and clearing values
  • Best practices to create dashboards
  • Dashboard examples; using Tableau workspace and Tableau interface
  • Learning about Tableau joins
  • Types of joins
  • Tableau field types
  • Saving and publishing data sources
  • Live vs extract connection
  • Various file types
  • Need for Machine Learning
  • Introduction to Machine Learning
  • Types of Machine Learning
  • Machine Learning with Python
  • Applications of Machine Learning
  • Introduction to Supervised Learning
  • Introduction to Regression
  • Understanding a Decision Tree
  • Impurity Function
  • Entropy
  • Concept of Information Gain for Node Splitting
  • Types of unsupervised learning, such as clustering and dimensionality reduction, and the types of clustering
  • Introduction to k-means clustering
  • Math behind k-means
  • Dimensionality reduction with PCA
  • Introduction to Natural Language Processing (NLP)
  • Introduction to text mining in Python with ML
  • Importance and applications of text mining
  • How NLP works with text mining
  • Writing and reading to word files
  • Language Toolkit (NLTK) environment
  • Text mining: Its cleaning, pre-processing, and text classification
  • Introduction to Deep Learning with neural networks
  • Biological neural networks vs artificial neural networks
  • Understanding the perceptron learning algorithm
  • Introduction to Deep Learning frameworks
  • TensorFlow constants, variables, and placeholders
  • Correct execution of the project
  • Scope control
  • WBS creation
  • Requirements collection
  • Creating a project team
  • Project governance
  • Influence of an organization on project management
  • Project life cycle
  • Project stakeholders
  • Importance of the project team
  • Project Scope Management
  • Project Cost Management
  • Project Resource Management
  • Project Risk Management
  • Project Stakeholder Management
  • Project Integration Management
  • Project Schedule Management
  • Project Quality Management
  • Project Procurement Management
  • Introduction of SAS software
  • Industries using SAS
  • Components of SAS System
  • Architecture of SAS system
  • Functionality of SAS System
  • Introduction of SAS windows
  • Module 1: Working in the SAS Environment
  • Module 2: Creation of Database from Raw Data
  • Module 3: Output Delivery System
  • Module 4: Combining Datasets
  • Module 5: Functions
  • Module 6: Loops in SAS: Do Loops
  • Module 7: Array
  • Module 8: Procedures
  • Module 9: Graphs
  • Module 10: Proc SQL
  • Module 11: Macro
  • Why Should You Learn Post Graduate Certification Program in Data Science & AI in Hughes Springs Training?

    The annual salary of an Post Graduate Certification Program in Data Science & AI in Hughes Springs is $125k.

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    Students Reviews

    Read genuine student reviews on ZebLearn’s expert-led SAP training and foreign language courses. Hear success stories from learners who gained certifications, skills, and career growth!

    Rajeev Singh
    Rajeev Singh
    Data Scientist
    The Post Graduate Program in Data Science from Zeblearnindia was a transformative experience. The curriculum was well-structured and covered everything from basic data analysis to advanced machine learning techniques. The hands-on projects and real-world case studies were particularly beneficial. This program has equipped me with the skills needed to excel in the data science field.
    Meera Nair
    Meera Nair
    Business Intelligence Analyst
    Zeblearnindia’s Post Graduate Program in Data Science exceeded my expectations. The course provided a thorough understanding of data science principles and practical skills in programming, machine learning, and data visualization. The faculty was knowledgeable, and the capstone project offered valuable real-world experience. This program has been instrumental in advancing my career in data science.
    Subhendu Roy
    Subhendu Roy
    Data Analyst
    The Post Graduate Program in Data Science at Zeblearnindia was an excellent learning experience. The programs comprehensive approach to data science, from data preprocessing to machine learning, was very informative. The practical assignments and projects helped me apply theoretical concepts to real-world problems. I now feel confident in my ability to analyze and interpret complex data.
    Aditi Mehta
    Aditi Mehta
    Machine Learning Engineer
    Completing the Post Graduate Program in Data Science with Zeblearnindia was a great decision. The course covered a wide range of topics essential for a career in data science. The emphasis on practical skills and real-world applications made the learning process engaging and effective. This program has significantly enhanced my skills in data science and machine learning.
    Vivek Kumar
    Vivek Kumar
    Data Science Consultant
    The Post Graduate Program in Data Science from Zeblearnindia provided a solid foundation in data science. The program was well-organized and included in-depth training in data analysis, visualization, and machine learning. The capstone project was particularly valuable, allowing me to apply my skills to a real-world scenario. This program has been crucial in advancing my career in data science.
    Kiran Desai
    Kiran Desai
    Senior Data Analyst
    The Data Analytics training at Zeblearn India was exceptional. The instructors were knowledgeable, and the practical sessions helped me master various analytics tools and techniques. This course has significantly improved my analytical skills.
    Aditya Mehta
    Aditya Mehta
    Data Manager
    Zeblearn India’s Data Analytics course is comprehensive and well-structured. The hands-on approach and real-world examples made the learning experience engaging and effective. I highly recommend it to anyone looking to enhance their data analytics skills.
    Sneha Kapoor
    Sneha Kapoor
    Data Analyst
    The Data Analytics training I received at Zeblearn India was outstanding. The course content was relevant, and the instructors provided clear explanations. I now feel more confident in performing complex data analyses.
    Arjun Sharma
    Arjun Sharma
    Reporting Specialist
    The Data Analytics course at Zeblearn India exceeded my expectations. The curriculum covered all essential aspects, and the hands-on labs were extremely beneficial. It was a great learning experience.
    Rina Saha
    Rina Saha
    Business Intelligence Developer
    Zeblearn India’s Data Analytics training programs are excellent. The courses are well-structured, and the trainers are very supportive. I gained valuable skills that have helped me excel in my role.
    Aarav Kumar
    Data Scientist
    The Post Graduate Data Science training at Zeblearn India was exceptional. The curriculum was in-depth and covered advanced topics that are crucial for a career in data science. The hands-on projects were particularly beneficial in applying theoretical knowledge to real-world problems.
    Aditi Sharma
    Senior Data Analyst
    Zeblearn Indias Post Graduate Data Science course was comprehensive and highly informative. The instructors were industry experts, and the course content was up-to-date with the latest trends. This training has significantly enhanced my analytical skills.
    Rohan Mehta
    Data Scientist
    The Post Graduate Data Science training at Zeblearn India exceeded my expectations. The course was well-structured, and the practical exercises helped solidify my understanding of complex data science concepts. This training has been a great addition to my professional growth.
    Meera Rao
    Senior Data Scientist
    Zeblearn Indias Post Graduate Data Science training provided a deep and comprehensive understanding of the subject. The hands-on sessions were very engaging, and the instructors were knowledgeable and supportive. I now feel confident in tackling any data science challenges.
    Arjun Patel
    Data Scientist
    The Post Graduate Data Science course at Zeblearn India was exceptional. The training covered a wide range of topics, and the practical labs were particularly useful in understanding how to implement data science algorithms. This course has been instrumental in advancing my career.
    Kritika Gupta
    Data Science Consultant
    The Post Graduate Data Science training at Zeblearn India was highly effective. The course was detailed and provided a deep dive into data science techniques. The hands-on projects were especially beneficial in applying what I learned to real-world problems.
    Siddharth Kapoor
    Lead Data Scientist
    Zeblearn Indias Post Graduate Data Science training was an excellent learning experience. The course content was relevant and up-to-date, and the instructors were very knowledgeable. The practical exercises helped me gain a solid grasp of data science concepts.
    Aditi Desai
    Data Engineer
    The Post Graduate Data Science course at Zeblearn India was very comprehensive. The training provided a good balance of theory and practical application. The skills I gained have been invaluable in my role as a data engineer, allowing me to build more sophisticated models.
    Vikas Agarwal
    Research Scientist
    The Post Graduate Data Science training at Zeblearn India was outstanding. The course was well-structured, and the hands-on projects were very helpful in understanding the practical aspects of data science. This training has significantly improved my research capabilities.
    Neha Joshi
    AI Researcher
    The Post Graduate Data Science training provided by Zeblearn India was excellent. The instructors were very knowledgeable, and the course content was thorough. The hands-on exercises and real-world examples made the learning process engaging and effective.

    Post Graduate Certification Program in Data Science & AI in Hughes Springs - Flexible batches for you

    Date Type Schedule Time
    SOLD OUT 16 March 2025 Weekend SAT - SUN (08 Week) 18:00 To 20:00
    Filling Img 21 March 2025 Weekday MON - FRI (08 Week) 08:00 To 10:00
    26 March 2025 Weekend MON - FRI (08 Week) 10:00 To 00:00

    Price  1,20,000

    Now  95,000

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