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Data Analyst

Best Tally Training Institute in Delhi

Market View

Data Analysis becomes one of the most high-in-demand jobs around the world. As a result, a Data Analyst salary in India is significantly higher than other software related professionals.

Course Overview

Data Analytics refers to a quantitative and qualitative method and process which is used to increase the productivity and business profitability. It is a technique of extracting, acknowledging and analyzing information such as behavioral data, business patterns, and techniques which are dynamic and necessary for business. Every business organization needs to perform Data Analytics which can provide various benefits such as increased customer r satisfaction, enhancing the productivity and performance of the organization and can also provide the companies with the biggest growth opportunities.Data analysis is also considered an internal function of any business organization which deals with numbers and figures. Intercourse deep knowledge of recording and analyzing along with dissecting information and presenting the findings to make better decisions making for the management. However, in order to become a Data Analytics professional, one should also have knowledge in various Data Analytics tools such as Python, R-Programming, MS Excel and Access, Visual Basic for Application and Macros(VBA/Macros), SQL, Tableau and Business Intelligence tools. The best way to gain relevant skills and masteries Business Intelligence and Data Analytics tools is by attending quality Data Analytics Training in Delhi, Uttam Nagar, East Azad Nagar, Durgapuri, Kalkaji, Badarpur, Pitampura, Jaipur, Agra, Kanpur, Bhopal, provided by GDF Skills (a unit of Gayatri Devi Solution LLP). The Data Analytics Training Program is designed by industry experts that provides extensive and comprehensive expertise in different Data Analytics techniques and tools, allowing the participant to become an expert quickly. The Data Analytics with Python Overview, Tableau R-Programming Training Program will help the learners acquire expertise in predicting customer Trends and behavior, analysis, interpreting and delivering information in meaningful ways, driving effective decision making along with enhancing the business productivity. Anyone with a graduation degree is eligible to attend High-quality Data Analyst Training Course in Delhi which is specifically targeted towards both fresher’s and working professionals who want to enter into the field of Data Analytics or become fluent in Data Analysis techniques. The Data Analyst Certification Training is conducted by highly certified subject matter experts with the word 10 to 15 years of experience in the relevant field.

About Best Data Analyst Training in Delhi

The Data Analyst Training Course in Delhi is a dedicated and intelligently designed Data Analytics Training Course that is targeted towards an individual who is good with the numbers and figures and wants to lead a successful career in the Data Analytics field. The entire Data Analyst Training is divided into Six different modules which can be completed within 200 hours. The major highlight of the Data Analytics Training Course is the Data A Analytics course content which is highly updated as per the industrial standards.Upon completion of the Data Analytics Training in Delhi, Uttam Nagar, East Azad Nagar, Durgapuri, Kalkaji, Badarpur, Pitampura, Jaipur, Agra, Kanpur, Bhopal, the participants will be able to interpret Data and Analyze results using Statistical Techniques and provide reports, develop It covers a wide range of advanced topics in Data Analytics which include Excel and VBA macros Analytics, SQL and MS Access, Python Data Visualization, Tableau and MS Power Business Intelligence(BI), R-Programming & Python. Practical Training in Data Analytics will be provided to the learner to gain experience in Business Data Analysis and acquiring technical expertise in data models, segmentation techniques, data mining techniques, database design development, etc. It will also help them to develop strong analytical skills allowing them to easily collect, organize and analyze a large amount of information with accuracy and efficiency and implement databases, Data Analytics and data collection system along with other strategies to increase the efficiency and quality, gathering information from primary and secondary data sources, identifying and analyzing latest trends and patterns, filtering the information by reviewing the computer reports and performance indicators to identify any issues and much more. The participants will also be provided with in depth understanding of Data Analysis using live projects and assignments on Real world cases so that they would not require additional Data Analytics Training after getting placement. Proper workshops and handouts will be conducted by the teachers to offer them industry relevant knowledge and resolve any of their issues. There are various other job profiles which an individual can pursue after completing the Data Analytics Training in Delhi, Uttam Nagar, East Azad Nagar, Durgapuri, Kalkaji, Badarpur, Pitampura, Jaipur, Agra, Kanpur, Bhopal, which include Data Analyst, Senior Analyst, Data Manager, Data Scientist, Business Intelligence professional, etc. Upon completion of the Data Analyst training, valid certification in Data Analytics will be awarded to all the learners to help them gain the competitive edge over other candidates during the interview. 100% Job Placement Assistance will also be provided to the candidates who successfully cleared the Data Analytics Training Course by providing them resume building services along with mock interviews to make them ready for entering into the same market.

Why any one choose Data Analyst course or career option

Over the last few years, Data Analytics has become one of the most important business functions worldwide due to technical Advancement which has resulted in increased generation of information on a regular basis. Every business organization regardless of their scale and size requires data in order to identify the behavior and trends in the Business Industry which can be used to make better decision making and satisfy the needs of the customers. . This has also increased the requirement for professional and knowledgeable Data Analysts in the market. There are various other reasons why one should learn Data Analytics and enter into the field. Some of these reasons are mentioned below With the continuous generation of information which is expected to increase 50 times by 2020, there is an immense shortage of talent of skilled candidates. Professionals who are able to analyze the information will have increased requirement in the future. According to Gartner, there will be three hundred percent increase in the jobs related to Data Analytics by the year 2018. This is another reason to Learn Data Analytics as it will offer the individual better job security. Data Analytics is one field which is not restricted by one industry. Therefore, after acquiring skills and expertise in Data Analysis, you can land a job at various Business Industries such as E-commerce, Real-Estate, telecommunication, healthcare, retail and much more. One of the best reasons to learn Data Analytics is the kind of payscale received by the professional. Starting salary for Data Analytics for freshers can be 3 lakh per annum as of 2018 which can go up to 10 lakh per annum as per the experience. There is also great growth opportunities in the Data Analytics field as you will be working with various other professionals. You will get to learn new things which will be beneficial for your career in the future

Data Analyst Training Programmed Modules

Data Analytics Training Course

  1. Introduction to Excel
    • Basic Understanding Menu and Toolbar, Introduction to different category of functions like Basics, Mathematical and Statistical, Date and Time, Logical, Lookup and References, Text and Information.
  2. Mathematical Functions
    • Sum, Sumif, Sumifs, Count, Counta, Countblank, Countif, Countifs, Average, Averagea, Averageif, Averageifs, Subtotal
    • Aggregate, Rand, Randbetween, Roundup, Rounddown, Round, Sumproduct
  3. Date and Time Function
    • o Date, Day, Month, Year, Edate, Eomonth, Networkdays, Workday, Weeknum, Weekday, Hour, Minute, Second, Now, Today, Time
  4. Text Functions & Data Validation
    • Char, Clean, Code, Concatenate, Find, Search, Substitute, Replace, Len, Right, Left, Mid, Lower, Upper, Proper, Text, Trim, Value, Large, Small Filters (Basic, Advanced, Conditional), Sort (Ascending, Descending, Cell/ Font Color), Conditional Formatting, Data Validation, Group & Ungroup, Data split.
  5. Statistical Function & Other Functions
    • Isna, Isblank, Iserr, Iseven, Isodd, Islogical, Isytext, Max, Min, Len, Right, Left, Mid, ,Maxa, Maxifs, Median, Minifs, Mina, Vara, Correl, Geomen
  6. Logical Functions
    • And, Or, If, Iferror, Not, Nested If
  7. Lookup & Reference Functions
    • VLookup, HLookup, Index, Match, Offset, Indirect, Address, Column, Columns, Row, Rows, Choose, Arrays Concept In Lookup Formula's, Past Special, Past link
  8. Pivot Table - MIS, Data Analysis & Visualization
    • Pivot Table- What-if Analysis, Data Table -One Variable and Two Variables, Data Analysis Using Statistics, Descriptive Statistics, ANOVA, Moving Average, Testing Hypothesis, Measuring Covariance and Correlation, Distribution, Regression, Graphs & Charts, Analysis Tool Pack, Solver, Histogram, Pareto, Water Fall, Import and Export data, Protect/Unprotect sheets/workbooks, Worksheet formatting and Print Display
  9. Data Collection Method
    • With Data Quality, Collaboration & Security Like Share Your Workbook On Share Drive With Quality
  10. Analysis Single/Multidimensional Analysis
    • Like Three Dimensional (3D) Tables, Sensitive Analysis Like Data Table, Manual What-If Analysis, Threshold Values, Goal Seek, One-Variable Data Table, Two-Variable Data Table
  11. Advanced Dashboard in Excel
    • Overview of Chart types, Chart Formatting, Active X Form Controls, Principle of great dashboard design, Selecting Correct Chart to display data, Interactive Charts with Form Controls, Combo box, Check Box, Scroll Bar and Radio Button, Interactive Dashboard with Form Controls, Form Controls for reports automation, Data Models using Power Pivot
  12. Two Live Report Development in Advanced Excel
    • (Real World Data)
  1. To Define KPIs (Key performance Indicator), Customer Service Dashboards or Project Management Dashboard (Gantt Chart)
  2. Dashboard Reports Based on Tables and Number or Charts/Graphs or Both.
  3. Introduction to Programming Introduction to logical thinking flowcharts & algorithms
  4. Define Objective, Start & End Points; Identifying Solution & Breaking it Into Sequential Steps Writing a Algorithm
  5. Step-by-Step Instructions, Flowcharts, Process Flow Diagrams. Excel Macros - an Introduction
  6. Complete Review of the VBA Language (Subs, Functions, Variables, Arrays, Loops, Logic. etc.)
    • Excel Macro Language Review (VBA) Including Variables, Data Types, Constants, Arrays, Operators, Expressions, Loops, Logic Decisions And Calling
    • Overview Of Commanding Excel Using VBA Including A Discussion Of Objects, Properties And Methods
    • The Power of Macros - What, When, How to use Macros.
    • Introduction to Object Oriented Programming
    • Objects, Its Functions, Methods and Properties Introduction to Events
    • Details of Events, How & When to use of Events, Preparing to 'Macro' Visual Basic Editor (VBE) - Developer Tab, Security
    • Introduction to the VBE, Properties window, Project Explorer, Password Protection of Code How to use the VBE - Features, Options, Intelligence Technology
    • Debugging Mode, Bookmarks, Breakpoints, Watch Window, Immediate Window and Locals Window Inbuilt VBE Help Feature - Tips and Tricks
    • Form Controls vs. ActiveX Controls Getting into the Code
    • Message Box and Input Box Working with Data in Excel through VBA
    • Data Types, Constants and Variables
    • Different type of data type; How and When to use Variables to Store Information.
    • For-Next, For-Each, Do-While, Do until, Do Loop Decision-Making and Code Branching
    • If-Then-Else, Select-Case, And/or Nested Conditions
    • What is user's Defined Functions?
    • How to create & use them.
    • Use of Arrays in VBA programming with one dimensional, two dimensional or multi-dimensional analysis
  7. Excel VBA Power Programming For VBA Macros
    • Working with Dynamic Ranges. Protecting Worksheets, Cells and Ranges. Working with Multiple Files. Opening & Saving Files
    • How to Analyze Data On Multi Worksheets And Build Summary Sheets
    • How to Access The Windows File And Folder System To Open And Close Workbooks
    • How to Protect Your Code Against Errors
    • How to Use Excel And VBA To Create Basic Dash Boards
    • How to Create Your Own Custom Business Worksheet Functions In VBA
    • How to Create Basic Report Generation Tools Using Excel VBA, Microsoft Word And PowerPoint
    • How to Use The Excel Visual Basic Macro Recorder To Record Excel Tasks In VBA And Then Interpret The Code
  8. Overview of Using User forms To Create Business Wizards
    • Working with User Forms & User Forms Events like List box, Combo box, Option Buttons, Check box, Text box, Labels, Command button, Toggle button.
    • How to create dynamic dashboard on user form with different controls
    • How to link various user form with each other to create a complete interface between user and system
  9. Connection between Excel VBA & other platforms
    • How to Establish Connection Between VBA and Internet Explorer to Open any Internet Website through VBA
    • How to Establish Connection Between Excel VBA and power presentation to create power point through VBA
    • How to Establish Connection Between Excel VBA and Access database to update the data in access through VBA
    • How to Establish Connection Between Excel VBA and outlooks through VBA
    • How to Establish Connection Between Excel VBA and MS Word through VBA
  10. Testing and Debugging Your Code
    • Types of Errors
    • Using Breakpoints
    • Debugging Techniques
    • Dialogue Boxes
    • UsingMsgBox Function
    • UsingInputBox Function
    • Working with FileDialog
    • Working GetOpenFilname Method
    • Working GetSaveAsFilename Method
  11. Effective Error Handling
  12. Automation Development Reports & Live Projects
  1. Introduction to SQL
    • SQL Course overview
    • Installing the test environment
    • What is SQL?
    • Editors and Platforms to learn SQL
  2. Complete SQL in a Class
    • Quick-start introduction
    • Using the basic SELECT statement
    • Selecting rows
    • Selecting columns
    • Counting rows
    • Inserting data
    • Updating data
    • Deleting data
    • Import and Export data
  3. Fundamentals of SQL
    • Databases and tables
    • SQL syntax overview
    • Data Definition, Data Manipulation, Data Control, Transactional Control statements
    • Creating tables
    • Deleting a table
    • Inserting rows into a table
    • Deleting rows from a table
    • What is NULL?
    • Controlling column behaviors with constraints
    • Changing a schema with ALTER
    • Filtering data with WHERE, LIKE, and IN
    • Removing duplicates with SELECT DISTINCT
    • Sorting with ORDER BY
  4. How Relationships Work in SQL
    • Understanding joins
    • Accessing related tables with JOIN
    • Multiple related tables
  5. Explaining SQL Strings
    • About SQL strings
    • Finding the length of a string
    • Selecting part of a string
    • Removing spaces with TRIM
    • Making strings uppercase and lowercase
  6. Numbers and SQL
    • About numeric types
    • Finding the type of a value
    • Integer division and remainders
    • Rounding numbers
  7. SQL Functions and Clause
    • The Aggregate functions MIN, MAX, AVG, SUM and COUNT, UPPER, LENGTH, LOWER
    • The GROUP BY and HAVING clauses Grouping in a combination with joining
  8. Triggers in SQL
    • Concept of Trigger
    • Create Trigger for (Insert,Update,Delete)
    • Alter Trigger
  9. What are Subselects and Views in SQL
    • Creating a simple subselect
    • Searching within a result set
    • Creating a view
    • Creating a joined view
  10. Maintaining SQL Server Database
    • Backup Database
    • Restore Database
  11. SQL Server Job Creation
    • How to create job in SQL Server Agent
    • How to schedule job
  12. Access environment and tools
  13. Database terminology and concept
  14. Designing database in Access
    • Join Tables That Have No Common Fields
    • Work with Subdatasheets
    • Create Sub queries
  15. Working with the runtime of Tables
  16. Data migration and importing
  17. Working with the Design side of Queries
  18. Working with the runtime of Queries
  19. Working with the Design side of Forms
    • Adding Controls to Forms
    • Creating Sub forms
    • Organizing Information with Tab Pages
    • Displaying a Summary of Data in a Form
    • Applying Conditional Formatting
  20. Working with the runtime of Forms, Managing Switchboard
  21. Understanding RDBMS
  22. Working with the Design side of Tables
    • Create Query
  23. Working with the Design side of Reports
    • Organize Report Information
    • Format Reports Include Charts in a Report
    • Add a Calculated Field to a Report
    • Add a Sub report to an Existing Report
  24. Working with the runtime of Reports
  25. Working with the Design side of Macros
    • Creating a Macro Restricting Records Using a Condition
    • Automating Data Entry Using a Macro
  26. Working with the runtime of Macros
  27. How to create a functional specification
  28. Build a real-world business application
  29. Putting altogether and deployment

    Module 4 A) Tableau

    1. Getting Started with Tableau
      • Overview Of Tableau
      • Tableau Architecture
      • Installation And Configuration Of Tableau 10
    2. Connecting to The Data
      • Managing Metadata
      • Managing Extracts
      • Data Sources
      • Cross-Database Joins
      • Data Aggregation And Data Ports
      • Tableau Charts
      • Bar Charts and Stacked Bars Data Blending
      • Tree Maps and Scatter Plots
      • Individual Axes, Blended Axes, Dual Axes and Combinational Chart
    3. Visual Analytics
      • Drill Down and Hierarchies
      • Sorting, Filtering and Grouping
      • Trend and Reference Lines
      • Forecasting and Clustering
      • Analysis with Cubes and MDX
    4. Developing First Bar Char
      • Connecting Tableau to Data File
      • Navigating Tableau
      • Calculated Fields
      • Adding Colors, Labels and Formatting

    Module 4 B) MS Power BI

    1. A Quick Introduction
      • What is MSPBI and its scope
      • Learn the common work flow in MSPBI
      • Building blocks of MSPBI and its relations
      • Quick demo how to create a business dashboard in MSPBI
      • MSPBI components
      • Old vs. new technologiesnecting to The Data
      • Power BI Desktop/Service/Mobile
    2. Getting Business Data
      • Get data in shape for use with MSPBI
      • Combining two or more data sets (source data) for reporting
      • Tackling messy data in MSPBI
      • Clean and transform data
    3. Modelling in Power BI
      • How to connect many different data sources
      • Manage data source (database) relationships
      • Unique keys
      • Calculated columns and more
      • Custom calculations to evaluate time-based functions
      • Build calculated tables based on DAX formulas and expressions
      • Creating and viewing visuals easier with optimized models
      • Discover hierarchal drill-down tools for date fields
    4. Data Visualization
      • Create and customize visualization in MSPBI and its power
      • Use combination charts

    Time Series, Maps and Aggregation

    • Data Extracts and Time Series
    • Understanding Granularity, Aggregation and Level of Details
    • Default Location in Maps
    • Custom Geo Coding
    • Symbol Map and Filled Map

    First Dashboard

    • Into Section
    • Joining Data In Tableau
    • Working With Maps and Hierarchies
    • Scatter Plot and Applying Filters in Different Sheets
    • Creating First Dashboard

    Blending Data and Dual Axis Charts

    • Duplicate Values
    • Multiple Fields
    • Data Blending
    • Dual Axis Chart
    • Building Calculated Fields

    Table Calculation and Storytelling

    • Downloading Dataset and Connection
    • Mapping
    • Building Table Calculation For Gender
    • Bins and Distributions for Age
    • Tree Map Chart
    • Advanced Dashboard
    • Storyline and Storytelling

    Data Preparation

    • Data Format
    • Data Interpreter
    • Multiple Columns And Pivot
    • Metadata Grid
    • Advanced Data Preparation
    • Create and format slicers with it
    • Map visualizations and its utilization
    • Use tables and matrixes
    • Long live bubbles
    • scatter charts in action
    • Advanced funnel and waterfall charts
    • Drive fast dashboard insights with gauges and numbers
    • Color your visualization world with colors
    • shapes and scales
    • Adding personal touch
    • logo etc. to reports and dashboards
    • Display and present your dashboard in a way you want with summarize data
    • Control how your report elements overlap with each other
    • Learn to drill into hierarchies
    • Manage how levels are shared (Z-order in reports)
    • How to use R visuals in MSPBI

    Data Exploring & Sharing

    • Quick insights in Power BI Service
    • Create and configure a dashboard
    • Share dashboard with your organization
    • Display and edit visuals- tiles
    • full screen
    • Get more space on your dashboard
    • Install and configure a personal gateway
    • Excel and MSPBI
    • Import and excel table into Power BI
    • Import excel files with data models and power view sheets
    • Connect One Drive for business to MSPBI
    • Excel data in Power BI summary

    DAX (Data Analysis Expression) Application

    • DAX and its basic building blocks
    • Create calculated columns (fields) and measures in MSPBI using DAX formulas
    • Breadth of functions available in DAX
    • User variables in DAX
    • Create expressions across multiple tables with relational functions
    • Filter and evaluate tables using advanced table functions


    Module 5 A) R-Programming

    1. Introduction to Business Analytics
      • Introduction to Business Analytics
      • Types of Analytics
      • Case study on Walmart, Signet Bank
      • Data Science and its importance
    2. Introduction to R
      • Introduction to R
      • Installing R
      • Installing R Studio
      • Workspace Setup
      • R Packages
    3. R Programming
      • R Programming
      • if statements
      • for statements
      • while statements
      • repeat statements
      • break and next statements
      • switch statement
      • scan statement
      • Executing the commands in a File
    4. R Data Structure
      • Data structures
      • Vector
      • Matrix
      • Array
      • Data frame
      • List
      • Factors
    5. Apply Functions
      • DPLYR & apply Function
      • Import Data File
      • DPLYP - Selection
      • DPLYP - Filter
      • DPLYP - Arrange
      • DPLYP - Mutate
      • DPLYP - Summarize
    6. Apply Functions
      • Data visualization in R
      • Bar chart, Dot plot
      • Scatter plot, Pie chart
      • Histogram and Box plot
      • Heat Maps
      • World Cloud

    Module 5 B) Python

    1. Introduction to Python
      • Python Overview
      • Advantages and Disadvantages
      • Installation and Configuration
      • Interpreted Languages
    2. Programming with Python
      • Python Script
      • Standalone Scripts Under Unix And Windows
      • Using Variables and Operators
      • Command Line Parameters
      • Understanding Expressions
    3. Flow Control
      • If And Else If Statement
      • The While and Loop Statement
      • Continue Statement
      • Break Statement
      • Range () Function
      • Using Lists
    4. Sequence Data
      • List Operations and Methods
      • Sets
      • Dictionaries
      • Tuples
      • Strings
    5. Functions
      • Defining Functions
      • Parameters and Variables
      • Using Global Statement
      • Keyword Arguments
      • Keyword Only Parameters
      • The Return Statement
      • Varargs Parameters
      • Docstrings
    6. Errors and Exception Handling
      • Dealing Syntax Errors
      • Exception Handling
      • Cleaning Up
    7. Modules
      • Creating Modules
      • The From and Import Statement
    8. Apply Functions
      • Introduction to statistics
      • Type of Data
      • Distance Measures (Similarity, dissimilarity, correlation)
      • Euclidean space
      • Manhattan
      • Minkowski
      • Cosine similarity
      • Mahalanobis distance
      • Pearson’s correlation coefficient
      • Probability Distributions
    9. Hypothesis Testing I
      • Hypothesis Testing
      • Introduction
      • Hypothesis Testing – T Test, Anova
    10. Hypothesis Testing II
      • Hypothesis Testing about population
      • Chi Square Test
      • F distribution and F ratio
    11. Regression Analysis
      • Regression
      • Linear Regression Models
      • Non Linear Regression Models
    12. Classification
      • Classification
      • Decision Tree
      • Logistic Regression
      • Bayesian
      • Support Vector Machines
    13. Clustering
      • Clustering
      • K-means Clustering and Case Study
      • Logistic Regression
      • DBSCAN Clustering and Case study
      • Hierarchical Clustering
    14. Association
      • Association
      • Apriori Algorithm
      • Candidate Generation
      • Visualization on Associated Rules
      • Summary
      • Package
      • Dir Function
      • Module Name
    15. Importing and Exporting Data
      • Importing Data From Different Sources
      • Connecting To Databsem
      • Viewing Data Objects And Sets
      • Exporting Data To Other Formats
    16. Data Manipulation and Data Analysis
      • Cleansing Data With Python
      • Data Manipulation
      • Python Tools And In-Built Functions
      • Formatting And Normalizing Data
      • User Defined Functions
      • Data Analysis Using Statistics And Graphical Representation
    17. Data Structures and Regular Expressions
      • List, Tuples, Dictionaries And Set
      • Re Objects
      • Pattern Matching
      • Subexpressions
      • Parsing Data
      • Complex Substitutions
    18. Live Practice Sessions
      • Projects and Assignments
      • Live Training

Module 6 - PYTHON DATA SCIENCE & MACHINE LEARNING - 100% Free in Offer - by IIT/NIT Alumni Trainer
  1. Introduction to Python Programming for Data Analysis
    • Data types : int, float, str etc
    • Operations on data types
    • Data structures: list, dict, tuples, set
    • Iterators/iterables
    • functions
    • Pandas Dataframe, NumPy arrays
    • Data manipulation in pandas: slicing, subset, cross tabulation, aggregation

    Reference Data for running example

    • XYZ Company Dataset
    • Titanic

Module 6 A) Data Exploration

  1. Data Exploration
    • Univariate Statistics: mean, median, std.
    • Bivariate Statistics : correlation , covariance
    • Plots using Matplotlib,seaborn
    • Concepts of inferential statistics
    • Standardization
    • log transform
    • dummy variable creation
  2. Dimensionality Reduction + Linear Transformation
    • PCA
    • LDA
    • Matrix
    • Determinant
    • Matrix multiplication
    • Element wise operations

Module 6 B) Supervised Learning

  1. Supervised Learning
    • Linear classifier: logistic regression
    • Linear classifier: Support Vector Classifier
    • Tree Classifier: Decision Trees
    • Ensemble Classifier: RF, Boosted Trees
    • Cost Function minimization
    • Scikit learn library
    • Regression
  2. Machine Learning Concepts
    • Bias , Variance
    • Regularization
    • Hyperparameter tuning
    • Parametric / non parametric methods
    • cross validation
    • Sampling data

Module 6 C) Advanced Unsupervised Learning

  1. Advanced Unsupervised Learning
    • K means Clustering
    • t- SNE
    • Agglomerative Clustering

Module 6 D) Introduction to Text processing

  1. Introduction to Text processing
    • Tokenization
    • N-grams
    • TF-IDF
    • cosine similarity
    • Text Classification
    • Topic Modelling
    • spaCy library

Module 6 E) Introduction to Deep Learning

  1. Introduction to Deep Learning
    • Understanding a perceptron
    • Forward propagation
    • Backward propagation
    • Parameter update
    • CNN
    • RNN: GRU, LSTM
    • Regularization: dropout, data augmentation
    • Optimization: Momentum, Adam, AdamW
      • Data loading
      • gradient descent
      • Framework
    • PyTorch

Is there any requirement of Graduation Degree?

A bachelor's degree is needed for most entry-level jobs. Most data analysts will have degrees in fields like mathematics, finance, statistics, economics, or computer science. Strong math and analysis skills are needed.

Why should I learn Data Analytics?

If you enjoy working with numbers and figures, then Data Analytics can be the best possible career you can pursue as you will learn and master various Data Analytics tools which you will enjoy.

What are the job responsibilities of a Data Analyst?

A Data Analyst collects unstructured data from various sources in the form of numbers and converts it into plain English which can be understood by the management to make better business decisions.

What is the scope of data analyst in India?

Data Analytics is one of the fastest growing feelings in the Indian industry which have increased by 80% over the past few years. It is also expected to increase by 300% in the next few years or more.

What are the eligibility criteria to join the Data Analytics training course?

The only eligibility criteria to join the Data Analyst courses to have a graduate degree in any field. Having some sort of knowledge in programming would be beneficial but not mandatory.

Do you also provide flexible class timings?

Yes, students can take classes on weekdays and weekends as per their preference.

Whom I Contact to join or Where is the Location?

GDF Skills is located near Uttam Nagar East metro station Kindly contact to our counselor for advanced training & placement support. Area near to us are laxmi nagar, mandawali, shakarpur, patparganj, mayur vihar, ashok nagar, kaushambi, pandav nagar, geeta colony, shahadra, dilshad garden, bhajanpura, loni, paharganj, connaught place, kamla nagar, karol bagh, lajpat nagar, greater kaishal, neheru place, okhla phase, jamia, sarita vihar, dwarka, uttam nagar, vasundhara, indirapuram, ghaziabad. Those living near jahangirpuri, azadpur, shalimar bagh, wazirpur, pitam pura, rohini, rithala, nangloi, paschim vihar, punjabi bagh, kirti nagar, tilak nagar, rajouri garden, janakpuri visit to our Uttam Nagar training center / East Azad Nagar Kindly Note: GDF Skills Trainer helps you to provide best training in Uttam Nagar or East Azad Nagar, traveling of about 15-20 minutes helps to choose best institute.

Why GDF is best for Data Analyst?

  1. Govt. registered institute E-verified Data Analytics Diploma
  2. Industry acceptable Data Analytics certification for all learner's training which help fresher/Experienced to up-skill at corporate.

  3. Experienced Faculty
  4. Industry Expert Sr. Lead Analyst / Technical Analyst With 12+ Years provide workshop session @ GDF

  5. Placement Assistance
  6. GDF Skills provide 100% placement assistance after completion of Data Analytics training our dedicated placement team arrange interview till placement.

  7. Lab Facility
  8. Data Analytics Practical Training help to gain exposure like corporate level with technical test series

  9. Data Analytics Workshop Sessions
  10. Real time projects & best case study makes GDF Skills workshop very unique and lively for learners.

  11. Admin Support
  12. For Learner's, Our admin team fresh batch schedule/re-scheduling classes/arrange doubt classes.

Data analyst & science : Job Roles

Let us take a sneak peek into some of the Data Science job roles in demand. Data Science jobs for freshers may include the job of a business analyst, data scientist, statistician or data architect.

  1. Big Data Engineer
  2. Big data engineers develop, maintain, test, and evaluate big data solutions within organizations.

  3. Machine Learning Engineer
  4. Machine learning engineers have to design and implement machine learning applications/algorithms to address business challenges.

  5. Data Engineer/Data Architect
  6. Data engineers/architects develop, construct, test, and maintain highly scalable data management systems.

  7. Data Scientist
  8. Data scientists have to understand the challenges of business and offer the best solutions using data analysis and data processing.

  9. Statistician
  10. Statistician interprets the results, along with strategic recommendations or incisive predictions, using data visualization tools or reports.

  11. Data Analysts
  12. Data analysts are involved in data manipulations and data visualization.

  13. Business Analysts
  14. Business analysts use predictive, prescriptive, and descriptive analyses to transform complex data into easily understood actionable insights for the users.

Data Scientists: Average Salaries

S.No Chapter Name
Job Profile Annual Package (In Rupees)
Software Engineer 2,51,000 to 10,00,000
Data Analyst 1,97,000 to 9,12,000
Senior Software Engineer 4,64,000 to 20,00,000
Data Scientist 3,37,000 to 20,00,000
Software Developer 2,06,000 to 10,00,000
Sr. Software Engineer/Developer/Programmer 4,13,000 to 20,00,000
Senior Business Analyst 4,29,000 to 20,00,000
Business Analyst, IT 2,86,000 to 10,00,000
Senior Data Analyst 3,10,000 to 10,00,000

Top Recruiters

  1. Amazon
  2. LinkedIn
  3. IBM
  4. Walmart Labs
  5. Busigence Technologies
  6. Fractal Analytics
  7. Sigmoid
  8. Flipkart
  9. Mate Labs
  10. Couture