Data Analyst Training in Pune

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3RI Technologies Data Analyst Course in Pune is designed to provide you with comprehensive knowledge and skills in data analysis. Through hands-on training and industry-relevant projects, you will learn data manipulation, visualization, and statistical analysis techniques. This course will empower you to excel in the field of data analysis and make informed business decisions.

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EMI Plan Registration Percent
EMI
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10%
1st installment - 1st month of joining
70%
2nd installment - 2nd month
20%

Key Features

Course Duration : 6 Weeks

Real-Time Projects : 2

Project Based Learning

EMI Option Available

Certification & Job Assistance

24 x 7 Support

Data Analyst Syllabus

The detailed syllabus is designed for freshers as well as working professionals

1. Fundamentals of Data Science and Mathematical statistics
    ● Introduction to Data Science
    ● Need of Data Science
    ● BigData and Data Science
    ● Data Science and machine learning
    ● Data Science Life Cycle
    ● Data Science Platform
    ● Data Science Use Cases
    ● Skill Required for Data Science
2. Mathematics For Data Science
    ● Linear Algebra-Matrices
        o Zero
        o One
        o Identify
        o Diagonal
        o Column
        o Row
        o Operations
3. Introduction to Statistics
    ● Descriptive vs. Inferential Statistics
    ● Types of data
    ● Measures of central tendency and dispersion
    ● Hypothesis & inferences
    ● Hypothesis Testing
    ● Confidence Interval
    ● Central Limit Theorem
4. Probability and Probability Distributions
    ● Probability Theory
    ● Conditional Probability
    ● Data Distribution
    ● Distribution Functions
        o Normal Distribution
        o Binomial Distribution

1. Using a Spreadsheet
    ● What is Excel?
    ● Why Use Excel?
    ● Excel Overview
    ● Excel Ranges, Selection of Ranges
    ● Excel Fill, Fill Copies, Fill Sequences,Sequence of Dates
    ● Excel adds, move, and delete cells
    ● Excel Formulas
    ● Relative and Absolute References
2. Functions
    ● SUM
    ● AVERAGE
    ● COUNT
    ● MAX & MIN
    ● RANDBETWEEN
    ● TRIM
    ● LEN
    ● CONCATENATE
    ● TODAY & NOW
3. Advanced Functions
    ● Excel IF Function
    ● Excel If Function with Calculations
    ● How to use COUNT, COUNTIF, and
    ● COUNTIFS Function?
4. Data Visualization
    ● Excel Data Analysis – Data Visualization
    ● Visualizing Data with Charts
    ● Chart Elements and Chart Styles
    ● Data Labels
    ● Quick Layout

    ● An Introduction to RDBMS & SQL
    ● Data Retrieval with SQL
    ● Pattern matching with wildcards
    ● Basics of sorting
    ● Order by clause
    ● Aggregate functions
    ● Group by clause
    ● Having clause
    ● Nested queries
    ● Inner join
    ● Multi join
    ● Outer join
    ● Adding and Deleting columns
    ● Changing column name and Data Type
    ● Creating Table from existing Table
    ● Changing Constraints Foreign key

    ● TRIM
    ● LEN
    ● CONCATENATE
    ● TODAY & NOW

1. An Introduction to Python

    ● Why Python , its Unique Feature and where to use it?
    ● Python environment Setup/shell
    ● Installing Anaconda
    ● Understanding the Jupyter notebook
    ● Python Identifiers, Keywords
    ● Discussion about installed module s and packages
2. Conditional Statement ,Loops and File Handling
    ● Python Data Types and Variable
    ● Condition and Loops in Python
    ● Decorators
    ● Python Modules & Packages
    ● Python Files and Directories manipulations
    ● Use various files and directory functions for OS operations
3. Python Core Objects and Functions
    ● Built in modules (Library Functions)
    ● Numeric and Math’s Module
    ● String/List/Dictionaries/Tuple
    ● Complex Data structures in Python
    ● Python built in function
    ● Python user defined functions
4. Introduction to NumPy
    ● Array Operations
    ● Arrays Functions
    ● Array Mathematics
        ▪ Mean
        ▪ Standard Deviation
        ▪ Max
        ▪ Min
    ● Array Manipulation
        ▪ Reshaping
        ▪ Resizing
    ● Array I/O
    ● Matrix creation
    ● Data generation
    ● Random function
    ● Normalized Data Generation
    ● Indexing and Slicing
    ● Transpose
    ● Importing Files with Numpy
5. Data Manipulation with Pandas
    ● Data Frames
    ● Series
    ● I/O
    ● Creating Pandas DataFrame
    ● Selection in DFs
    ● Data Describe
    ● Data info
    ● Retrieving in DFs
    ● Applying Functions
    ● Reshaping the DFs – Pivot
    ● Combining DFs Merge Join
    ● Data Alignment
6. SciPy
    ● Hypothesis Testing using Scipy
    ● Shapiro Test
    ● Spearmaman Test
    ● T-Test of Independents
    ● Chi-Square Test
7. Visualization with Seaborn
    ● Seaborn Installation
    ● Introduction to Seaborn
    ● Basics of Plotting
    ● Plots Generation
    ● Visualizing the Distribution of a Dataset
    ● Selection color palettes
8. Visualization with Matplotlib
    ● Matplotlib Installation
    ● Matplotlib Basic Plots & it’ s Containers
    ● Matplotlib components and properties
    ● Scatter plots
    ● 2D Plots
    ● Histograms
    ● Bar Graphs
    ● Pie Charts
    ● Box Plots
    ● Customization
    ● Store Plots
9. Descriptive Statistics
    ● Data understanding
    ● Observations, variables, and data matrices
    ● Types of variables
    ● Measures of Central Tendency
    ● Arithmetic Mean / Average
        ▪ Merits & Demerits of Arithmetic Mean and Mode
        ▪ Merits & Demerits of Mode and Median
        ▪ Merits & Demerits of Median Variance
10. Probability Basics
    ● Notation and Terminology
    ● Unions and Intersections
    ● Conditional Probability and Independence
11. Probability Distributions
    ● Random Variable
    ● Parameters vs. Statistics
    ● Binomial Distribution
    ● Central Limit Theorem
12. Tests of Hypothesis
    ● Large Sample Test
    ● Small Sample Test
    ● One Sample: Testing Population Mean
    ● Hypothesis in One Sample z-test
    ● Two Sample: Testing Population Mean
    ● One Sample t-test – Two Sample t-test
    ● Paired t-test
    ● Hypothesis in Paired Samples t-test
    ● Chi-Square test
13. Data Analysis
    ● Case study- Netflix
    ● Deep analysis on Netflix data

1. Machine Learning Features & Services
    ● Using python in Cloud
    ● Understanding Google Colab
    ● Understanding Jupyter Lab
    ● How to access Machine Learning Services
    ● Lab on accessing Machine learning services
    ● Uploading Data
    ● Preparation of Data
    ● Applying Machine Learning Model

1. Power BI Introduction
    ● Data Visualization, Reporting
    ● Business Intelligence (BI), Traditional BI, Self-Serviced BI
    ● Cloud Based BI, On-Premise BI
    ● Power BI Products
    ● Power BI Desktop (Power Query, Power Pivot, Power View)
    ● The flow of Work in Power BI Desktop Power BI Report Server, Power BI
    ● Service, Power BI Mobile.
    ● Flow of Work in Power BI / Power BI Architecture
2. PBI Desktop Installation, PBI Desktop & Service Overview
    ● Power BI Desktop Installation
    ● Building Blocks of Power BI
    ● Datasets, Visualizations, Reports, Dashboards, Tiles
    ● Power BI Desktop User Interface
    ● Fields Pane, Visualizations pane, Ribbon, Views, Pages Tab, Canvas
    ● Overview of Power Query / Query Editor
    ● – Extract, Transform & Load Data
● Connecting to Data Sources, Establish
    ● a connection to the Excel source
    ● Importing Data into Power BI or Query Editor
    ● Transforming the Data using Power Query
    ● Model the Data using Power Pivot – Relationship View
  ● Creating New Measures and New Columns using DAX – Data View
    ● Visualizing the Data using Power View and Power Maps – Report View
    ● Saving and Publishing the Visuals or
    ● Reports
    ● Logon to Power BI Service
    ● View the Reports in PBI Service and Share the reports

3. Power Query
    ● Introduction to Power Query – Extract, Transform & Load
    ● Data Transformation, Benefits of Data Transformation
    ● Shape or Transform Data using Power Query
    ● Overview of Power Query / Query Editor, Query Editor User Interface
    ● The Ribbon (Home, Transform, Add Column, View Tabs)
    ● Advanced Editor – Complete ETL Code
    ● Saving Your Work – Appling ETL Changes – Loading into Power Pivot Model
4. Data-types and Filters in Power Query

    ● Datatypes, Changing the Datatype of a
    ● Column
    ● Filters in Power Query
    ● Auto Filter / Basic Filtering
    ● Filter a Column using Text Filters
    ● Filter a Column using Number Filters
    ● Filter a Column using Date Filters
    ● Filter Multiple Columns
5. Inbuilt Column Transformations
    ● Remove Columns / Remove Other Columns
    ● Name / Rename a Column
    ● Reorder Columns or Sort Columns
    ● Add Column / Custom Column
    ● Split Columns
    ● Merge Columns
    ● Transpose Columns

6. In-built Row Transformations
    ● Header Row or Use First Row as Headers
    ● Keep Top Rows, Keep Bottom Rows
    ● Keep the Range of Rows
    ● Keep Duplicates, Keep Errors
    ● Remove Top Rows, Remove Bottom Rows, Remove Alternative Rows
    ● Remove Duplicates, Remove Blank Rows, Remove Errors
    ● Group Rows / Group By

7. Merge Queries / Join Queries Header
    ● Merge Queries, Merge Queries as New
    ● Default Types of Joins / Join Kinds / Merge Type
    ● Left Outer (all from first, matching from second)
    ● Right Outer (all from second, matching from first)
    ● Full Outer (all rows from both)
    ● Inner (only matching rows)
    ● Left Anti (rows only in first)
    ● Right Anti (rows only in second)

8. Power Pivot Power BI Data Modelling –Relationship View
    ● Data Modelling Introduction
    ● Relationship, Need of Relationship
    ● Relationship Types / Cardinality in General
    ● One-to-One, One-to-Many (or Many-to-One), Many-to-Many
    ● AutoDetect the relationship, Create a new relationship, Edit existing relationships

9. Enhancing the Data Model – DAX

    ● What is DAX, Calculated Column, Measures
    ● DAX Table and Column Name Syntax
    ● Creating Calculated Columns, Creating Measures
    ● Calculated Columns Vs Measures
    ● DAX Syntax & Operators
    ● DAX Operators
    ● Date and Time Functions
    ● Logical Functions
    ● Text Functions
    ● Math & Statistical Functions
    ● Time Intelligence Functions

10. Power View Report View / Power View
    ● Report View User Interface
    ● Fields Pane, Visualizations pane, Ribbon, Views, Pages Tab, Canvas
    ● Visual Interactions
    ● Interaction Type (Filter, Highlight, None)
    ● Visual Interactions Default Behaviours, Changing the Interaction
    ● Filters in Power View
    ● Filter Types
    ● Visual Level Filters, Page Level Filters,Report Level Filters, Drill Through Filters
    ● Basic Filtering, Advanced Filtering, Top N,Relative Date Filtering
    ● Numeric field filters, Text field filters, Dateand Time field Filters
11. Power BI Visualizations
    ● Visualizing Data, Why Visualizations
    ● Visualization types, Field Wells
    ● Visuals for Filtering, Visualizing Categorical Data
    ● Visualizing KPI Data, Visualizing Tabular
    ● Data, Visualizing Geographical Data
    ● Leveraging Power BI Custom Visuals
12. Power BI Service [Administration]Power BI Service Introduction
    ● Power BI Service Introduction, Power BI
    ● Cloud Architecture
    ● Creating Power BI Service Account, SIGN IN to Power BI Service Account
    ● Publishing Reports to the Power BI
    ● service, Import / Getting the Report to PBI Service
    ● My Workspace / App Workspaces Tabs
    ● DATASETS, WORKBOOKS, REPORTS, DASHBOARDS.
    ● Working with Datasets, Creating Reports in Cloud using Published Datasets
    ● Power BI Datasets Live Connection using Desktop
    ● Working with Workbooks, working with Reports, Sharing Reports
    ● Dashboards Development
    ● Creating Dashboards
    ● Pin Visuals and Pin LIVE Report Pages to Dashboard
    ● Advantages of Dashboards
    ● Interacting with Dashboards
    ● Adding Tiles to Dashboards

13. Data Gateway
    ● Introduction to Data Gateways
    ● How Data Gateways work
    ● Connect to an on-premise Data Source by using a Data Gateway
    ● Download Data Gateway
    ● Installing a Data Gateway
    ● Types of Gateways
    ● On-premises Data Gateway, On-
    ● premises Data Gateway (personal mode)
    ● Row Level Security in Power BI

Resume Preparation & Project Discussion

Module Domain: Exploratory Data Analysis:
Case study:
Admission Prediction of Graduate Admissions from an Indian Perspective


Module Domain: Univariate and Multivariate Data Analysis:

Case Study:
Melbourne Housing, house price prediction data set

 

Module Domain:Bivariate Analysis and Pandas Profiling:
Case Study:
Suicide rates overview 1985 to 2016

 

Module Domain:Data Preprocessing: Dealing with missing values and outliers

Case study:
Data set which deals about some cities and states. Observe the data and see if you can recognize missing values or garbage values in the data set and handling them. Also treating the outliers in the data set.

 

Module Domain:Descriptive Statistics:
Case study:
Cardio Good Fitness

 

Module Domain:Applied Statistics:
Case Study:
Leveraging customer information for making business decisions.
Case Study:
Understanding factors for Churn in a Telecom Company


Module Domain:Visualization tools:

Case Study:

Sales forecast analysis

Case Study:

Marketing Campaign

Case Study:
Airline Fare case analysis

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Data Analyst Classes in Pune

A data analyst full course equips students with the expertise required to succeed in data analysis. Students receive the tools they need to gather, analyze, interpret, and present data effectively via data analyst training.It encompasses various disciplines, including data management, data extraction, visualization, and statistical analysis.

The first lesson in the best data analyst course covers the foundations of data analysis, including an overview of the various types of data, data gathering strategies, and data cleaning procedures. Pupils learn how to modify and analyze data sets using well-known data analysis applications and computer languages like Python or R. 

Statistical analysis is an essential component of the data analyst certification course, in which students study strategies for summarizing and interpreting data, conducting hypothesis tests, and discovering patterns and relationships in data. They also learn about predictive modeling and machine learning algorithms, which they will use to create data-driven predictions and judgments.

Data visualization is another important topic covered in the data analyst course in Pune. Students learn to use data visualization technologies such as Tableau and Power BI to create attractive visual representations of data. They look into different ways that data insights might be conveyed to both technical and non-technical audiences.

Additionally, pupils in the data analyst course with placement are taught how to handle data responsibly and by legal and ethical requirements, emphasizing the significance of data ethics and privacy.

Practical exercises and hands-on projects are used throughout the data analyst classes in Pune to provide students with real-world experience and to let them use what they have learned to solve business problems. A helpful learning atmosphere is fostered via collaborative activities and debates, which also promote the sharing of best practices.

Students who successfully complete the data analyst course are prepared to work as data analysts in various fields, including technology, marketing, finance, and healthcare. They are equipped to gather, examine, and analyze data to draw forth important insights and assist procedures for data-driven decision-making.

Now you know the data analyst course, let’s move forward about data analyst course in pune with placement

What does it mean by Short Term Job Oriented Data Analyst Course?

A short-term job-oriented data analyst course is a specialised education programme created to give students the skills and information they need to pursue a career in data analysis quickly. Usually between a few weeks to a few months, this course is designed to meet the unique demands of the labour market and aims to give students real, industry-relevant experience.

A short-term job-oriented data analyst course’s salient characteristics are as follows:

  • Core Data Analysis Skills: The main emphasis of these courses is on data analysis’s foundational ideas. In addition to learning how to gather, organise, and process data, students also learn how to draw insightful conclusions. Data visualisation and statistical analysis are frequently essential elements.
  • Statistical and Analytical Tools: Students learn about the software and statistical tools that are frequently used in the discipline, like R, Python, and Excel. For data analysis activities, proficiency with these technologies is essential.
  • Data Visualisation: In data analysis, it’s critical to communicate insights gained effectively. Using programs like Tableau or ggplot2 in R, these courses frequently teach data visualisation techniques that let students make eye-catching graphs and charts.
  • Real-world Projects: An essential element is practical experience. Students usually work on practical projects with real datasets and settings, which allows them to use their knowledge to address real-world issues. 
  • Work-specific Knowledge: Job-oriented data analyst courses frequently concentrate on the particular qualifications needed for jobs as data analysts across different industries. This could involve analytic methods and datasets unique to a given industry.
  • Job Placement Support: A lot of these courses assist students in finding employment by assisting with the creation of resumes, interview techniques, and job search tactics. This support is helpful for people who want to hire data analysts as soon as possible. 
  • Efficiency and Affordability: Short-term courses are available to anyone who wants to change occupations or upskill without committing to lengthy degree programmes since they are both time- and money-efficient.

For those looking to upgrade their current abilities or enter the data analysis sector immediately, a short-term job-oriented data analyst course is a wise option.

Graduates are well-prepared to work as data analysts in a variety of industries since their training emphasises transferable skills that are relevant to the demands of the data-driven labour market of today. These courses provide a clear route to a lucrative career in data analysis, regardless of your background—recent graduate, career changer, or professional seeking to progress in your existing position.

 

How the Short Term Job Oriented Data Analyst Course can help you in your career?

For your career, a short-term job-oriented data analyst course could be revolutionary. These programmes, which usually run only a few weeks to a few months, are meant to give you industry-specific, practical skills in a short amount of time. The main benefit is the quick development of fundamental data analysis skills, which are in high demand in the data-driven job market of today.

  • The fundamentals of data analysis, such as data collection, cleaning, manipulation, statistical analysis, and data visualisation, are covered in these courses. They frequently stress the need to be proficient with programs like Python, R, or Excel, which are crucial for data analysis jobs. 
  • Another essential element of these programmes is their hands-on component. Students work on real-world projects that mimic the difficulties that data analysts encounter daily. Through these initiatives, you can develop a portfolio that highlights your real-world talents and give potential employers concrete proof of your ability.
  • Furthermore, short-term data analyst courses frequently address the demands of particular job markets, guaranteeing that you have access to the newest methods and sector-specific expertise. Your versatility makes you a competitive applicant in various sectors, including e-commerce, healthcare, and finance.
  • In addition, many of the schools that offer these courses offer career services and assistance in finding employment. They help with job searching, interview preparation, and resume creation, which improves your chances of landing a data analyst position after graduation.
  • A short-term job-oriented data analyst course gives you the specific knowledge and abilities required for data analysis positions, which makes you a valuable asset to companies searching for data-savvy workers in today’s data-driven world, regardless of whether you’re looking to change careers or enter the workforce.

In today’s data-driven world, data analysts have many work opportunities. Here are a few possible employment options for data analysts:

1. Data Scientist

By utilizing cutting-edge statistical and machine learning approaches, data scientists concentrate on gaining insights and creating prediction models. They use massive datasets, design algorithms, and employ statistical models to tackle challenging issues and produce insights that may be put to use.

Job Responsibilities:

  • Extensive gathering, examination, and interpretation of large-scale complicated data sets
  • Combining statistical and machine-learning methods to find patterns and trends in data
  • Future forecasting and influencing business choices

Required Abilities: 

  • Background in computer science, statistics, or mathematics
  • Knows Python and R, among other programming languages


2.  Business Intelligence Analyst

In short, a job in business intelligence analytics is all about helping companies make decisions based on data by analyzing and figuring out what it all means. In this job, you’ll use various tools and methods to find insights into data and present them in a way that decision-makers can understand and use.

Job Responsibilities: 

  • Data collection and analysis to aid enterprises in making better decisions.
  • Using data visualization tools to present data in a manner that non-technical stakeholders can comprehend.

Required Abilities: 

  • Knowledge of commerce or a closely related field
  • BI and data warehouse applications such as Power BI and SQL, Excel, and Tableau knowledge.

 

  1. Data Engineer

Data engineers are responsible for designing and constructing data infrastructure and systems that facilitate the efficient collection, storage, and processing of data. They collaborate closely with data analysts and scientists to maintain data integrity, optimize data pipelines, and ensure data quality.

Job Responsibilities: 

  • Creating the infrastructure and systems needed for data collection, storage, and analysis
  • Maintaining and controlling vast databases and data sets
  • Ensuring the data’s safety, dependability, and accuracy

Required Abilities: 

  • Programming languages, including Python, Java, and SQL, with a high level of proficiency
  • Knowing Hadoop and Spark

  1. Business Analyst

Business analysts frequently collaborate with data analysts to identify data-driven solutions and comprehend business requirements. They analyze business processes, collect requirements, and utilize data to identify development opportunities, optimize operations, and support strategic decision-making.

Job Responsibilities:

  • Identifying and assessing business opportunities and problems
  • Generating ideas to improve business operations and performance
  • Communicating with stakeholders to gather requirements and offer suggestions

Required Abilities: 

  • Outstanding analytical and problem-solving skills
  • exceptional skills in presenting and communication
  • understanding of project management
  • Tableau, Excel, and SQL

 

5. Financial Analyst

Financial analysts look at market patterns and financial data to assist in making investment decisions, evaluating risks, and developing financial plans. They use financial statements, economic statistics, and market data to aid financial forecasts, valuation, and performance analysis. 

Job Responsibilities: 

  • Collecting and analyzing financial data to support business choices
  • Finding patterns and trends in financial data
  • Making estimates and financial models 

Required Abilities: 

  • Powerful analytical and problem-solving skills
  • Understanding of financial modeling and analysis
  • Strong presentation and communication skills
  • SQL, Excel, R, Python
  1. SQL

The most critical skill for data analysts is utilizing SQL or Structured Query Language, the industry’s de facto standard database language. People often call this language the “graduated” version of Excel because it can work with much bigger datasets than Excel can.

Almost every business requires a SQL expert to manage and store data, connect multiple databases (as Amazon does to recommend products), or construct or modify database architecture. The median salary for someone with advanced SQL skills is well over $75,000, and these skills are required for hundreds of job vacancies each month. Even non-techies can profit from this tool; however, knowing SQL is the first step if you want to work as a data analyst.

 

  1. Microsoft Excel

Excel has more analysis capabilities than just spreadsheets, even though that’s generally what comes to mind when you think of it. Complex Excel techniques like creating macros and VBA lookups are often used for lighter lifts and speedier analytics. Still, programming languages like R or Python are better suited to handle extensive data.

If you work for a small company or a new business, your information might have been made in Excel at first. Learning the application is essential because it has become a standard for businesses across all industries.  Fortunately, many excellent free online resources are available to get you started. There are also structured data analytics workshops available for people who want to learn more about the technology.

 

  1. Critical Thinking

The most challenging aspect of using data to acquire answers is frequently identifying the right questions to ask in the first place.If you want to succeed as an analyst, you must be able to think analytically.The task of a data analyst is to discover and combine obscure relationships. Although some of this capacity is innate, there are several methods you may utilize to develop your critical thinking skills.When seeking a solution, being grounded by asking yourself basic questions about the issue at hand prevents you from getting carried away with an unnecessary elaborate answer. . Furthermore, remember that you should think for yourself rather than depend on preexisting knowledge.

 

4. 
R or Python–Statistical Programming

R or Python can complete duties ten times faster and more efficiently than Excel. Excel cannot handle jobs that R and Python can.When working with massive data sets, these powerful statistical programming languages are used to do complex analysis and predictive analytics. They both follow the rules set by the industry.  To work as a data analyst, you must be fluent in at least one of these languages besides SQL.

Which one should you therefore learn?Because R and Python are free and open-source, employers typically do not care which programming language their employees employ as long as their analyses are accurate .However, because R was explicitly developed for analytics, some analysts prefer it to Python to work with data sets and conduct ad-hoc analysis.

  1. Data Visualization

You must be able to weave a compelling story out of relevant facts to support your claim and hold your audience’s attention. People will be challenged to persuade if your findings are difficult to identify immediately. As a result, the significance of your data can make or break your efforts at data visualization.  Analysts use eye-catching, top-notch charts and graphs to succinctly and effectively communicate their findings. Tableau’s visualization software is recognized as an industry-standard analytics tool because it is pleasantly user-friendly.

 

  1. Presentation Skills

Presentation skills and data visualization are interdependent..But it’s acceptable if public speaking presents some difficulties for you! The rare bout of performance anxiety affects even seasoned presenters. Just like with anything else, you should start by training until you find your rhythm. Forbes also recommends setting clear objectives for improvement and focusing on the audience rather than on oneself to feel more at ease when giving presentations.

  1. Machine Learning

Since artificial intelligence and predictive analytics are two of the trendiest topics in the field of data science, the ability to employ machine learning has been seen as an essential part of an analyst’s toolkit. Even if not all analysts employ machine learning, it is crucial to comprehend its principles and methods if you want to grow in the field. But to advance in this subject, you must first understand statistical programming.  You can start constructing machine learning models using an “out-of-the-box” tool like Orange.

Selecting the best data analyst certification institute is crucial if you’re thinking about a career in this area so that you can gain the skills and information required to succeed. This is where 3RI stands out, offering extensive and business-focused data analyst courses. If you’re looking for the top data analyst course institute in Pune, 3RI can be the same. Let’s talk about the benefits of choosing 3RI Institute.

  1. Professional and Skilled Faculty

3RI takes pride in its team of experienced and qualified faculty members who contribute substantial knowledge and practical expertise in data analysis.They are industry-certified and current on the most recent developments and technologies. . These experienced experts’ supervision and mentorship ensure a high standard of education and allow students to obtain valuable insights from real-world circumstances.

  1. Comprehensive and Industry-Relevant Curriculum

3RI’s data analyst course in Pimpri Chinchwad is a comprehensive program that encompasses all fundamental aspects of data analysis. The course ensures that students acquire a solid foundation in data analysis, from statistical analysis and data visualization to machine learning and programming languages such as SQL and Python. The curriculum is routinely revised to reflect the most recent industry trends and requirements, allowing students to acquire in-demand skills and remain marketable.

  1. Practical Methodology and Hands-On Teaching

3RI emphasizes the practical learning approach. Students can apply their knowledge and skills to address challenges with data analysis through the course’s practical instruction and real-world projects. This hands-on experience improves their ability to solve problems, and they get the self-assurance they need to face difficulties in the real world in their future employment.

  1. Tools and Technologies of the Industry

3RI ensures that students are trained on the tools and technologies used in data analysis that are standard in the business. This includes well-known programs like Tableau for visualizing data, Python and R for manipulating and analyzing data, and SQL for accessing databases. By learning how to use these tools well, students get the real skills employers need. 

  1. Placement Support and Career Guidance

By offering students career counseling and placement assistance, 3RI goes above and beyond. They advise how to prepare a resume, conduct an interview, and use job search tactics. Due to the institution’s partnerships with numerous businesses and organizations, students have more prospects to find good work opportunities. Students can transfer into their professional employment as data analysts more quickly because of this committed support.

  1. Options for Flexible Learning

3RI is aware of how crucial flexibility is for students. They provide both classroom-based and online training choices, letting students select the format that best fits their schedules and learning preferences. With this flexibility, busy professionals or those with other commitments can enroll in the course without sacrificing their current obligations.

  1. Positivity in Reputation and Views

Students and industry experts both have developed favorable opinions of 3RI. Positive reviews and comments from former students highlight the institution’s high-quality instruction, pragmatic approach, and welcoming learning atmosphere. This encouraging response demonstrates 3RI’s dedication to providing a top-notch learning experience.

Now you have data analyst course details, thus if you are looking for the best data analyst course in pune, 3RI Technologies is the best institute.

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31-Mar-24 | SAT-SUN 8:00 AM to 10:00 AM

15-Apr-24 | MON-FRI 8:00 AM to 10:00 AM

28-Apr-24 | SAT-SUN 8:00 AM to 10:00 AM

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Will I Get Certified?

Upon successfully completing this program, you’ll earn a certificate.

The 3RI certification is accepted and respected by every significant multinational company across the nation. Fresh graduates and corporate trainees are eligible for the assistance. We offer certificate once the academic and practical courses have been finished. The certification that we offer here at 3RI is recognized across the country. The value of your resume will grow as a result. With the assistance of this qualification, you will be able to obtain prominent employment posts in the most successful multinational corporations in the country. The completion of our course as well as the projects that are based on practical application, are prerequisites for receiving the certificate.

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FAQs

Most frequent questions and answers
After the course completion, an exam will be conducted to judge your knowledge along with the live project work completion check and you will be awarded a certificate from 3RI Technologies.
Yes, we provide placement assistance to our students. We have a dedicated team for Placement and tie ups with 300+ MNC’s and SME companies.
Yes we conduct demo classes every weekend. Please contact us for more details.
 
 

A degree in a relevant discipline, such as mathematics, statistics, or computer science, is required to become a data analyst. Improve your knowledge of data analysis tools, computer languages, and databases. Gain practical experience through internships or projects, and learn and adapt to changing trends in data analysis on an ongoing basis.

Salary ranges for data analysts depend on various criteria, including experience, geography, and sector. Data analysts may expect to make a competitive salary, especially given the rising demand for data-driven skills and insights across various industries.

 

Yes, data analysis frequently requires coding. Data analysts frequently use programming languages such as Python and R to manipulate, sanitize, analyze, and visualize data, as well as to construct models and automate data processes.

 

Qualifications for a data analyst typically include a degree in a relevant discipline (e.g., mathematics, statistics, computer science), strong analytical skills, programming language proficiency, and experience with data analysis tools and techniques.

 

Data analyst courses are academic programs that give students the knowledge and skills they need to work as data analysts. Topics like statistics, data manipulation, computer languages, and data visualization techniques are covered in these courses.

Yes, you can take a course in data analysis after getting your BBA, BCA, or BCS. These degrees give you a good background in business or computer science. You can add data analysis skills by taking more classes or getting a certification.

Yes, you most certainly can! Companies employ candidates who have completed courses and boot camps in data analytics. You can also apply for entry-level data analyst positions with a portfolio of completed assignments.

After becoming a data analyst, you can hone your skills in machine learning, data engineering, data visualization, advanced statistical analysis, or a particular industry or domain.

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