• Address D-126, Laxmi Nagar, Metro Station Gate No-5, Delhi-92
  • E-mail info@midmweb.com
  • Phone +918851104676

Schedule Your Demo


About Data Analysis

Data Analysis is a process of inspecting, cleaning, transforming, and modeling data to discover useful information, draw conclusions, and support decision-making. It plays a crucial role in various fields, including business, science, finance, healthcare, and more.

  • Data Collection: Gathering relevant data from various sources. This can include databases, surveys, sensors, social media, and other data repositories.
  • Data Cleaning and Preprocessing: Addressing issues such as missing values, outliers, and inconsistencies in the data to ensure its accuracy and reliability.
  • Exploratory Data Analysis (EDA): Examining and visualizing the dataset to understand its characteristics, identify patterns, and uncover insights. EDA often involves creating charts, graphs, and summary statistics.
  • Descriptive Statistics: Calculating basic statistical measures like mean, median, mode, standard deviation, and other metrics to summarize and describe the main features of a dataset.
  • Inferential Statistics: Making inferences and predictions about a population based on a sample of data. Hypothesis testing and confidence intervals are common techniques in inferential statistics.
  • Data Visualization: Representing data graphically using charts, graphs, and other visual elements to make complex patterns and trends more understandable.
  • Descriptive Statistics: Calculating basic statistical measures like mean, median, mode, standard deviation, and other metrics to summarize and describe the main features of a dataset.
  • Statistical Analysis: Applying statistical methods to analyze relationships between variables, test hypothesis, and make predictions about future observations.
  • Machine Learning: Utilizing algorithms and models to automatically learn patterns from data, make predictions, or classify information. Machine learning is often used for tasks such as regression, classification, clustering, and recommendation systems.
  • Big Data Analytics: Handling and analyzing large volumes of data that traditional databases may struggle to manage. Technologies like Hadoop and Spark are commonly employed in big Data Aanalytics.
  • Text Analysis and Natural Language Processing (NLP): Analyzing unstructured text data to extract insights, sentiment, and patterns. NLP techniques are used for tasks like text classification, sentiment analysis, and information extraction.
  • Predictive Modeling: Building models that use historical data to make predictions about future events. This involves selecting and training a model on a dataset and then using it to make predictions on new, unseen data.
  • Data Mining: Exploring large datasets to discover hidden patterns, relationships, or trends. Data mining techniques include clustering, association rule mining, and anomaly detection.
  • Real-time Analytics: Analyzing data as it is generated or received, allowing for immediate decision-making. Real-time analytics is crucial in applications where timely insights are essential.
  • Interpretation and Communication: Interpreting the results of the analysis and communicating findings to stakeholders. This often involves creating reports, dashboards, and presentations.

Recognizing and addressing ethical concerns related to Data Analysis, such as privacy issues, bias, and the responsible use of data. Data Analysis is an iterative and dynamic process that requires a combination of domain knowledge, statistical expertise, and proficiency in relevant tools and technologies. The goal is to transform raw data into actionable insights that can inform decision-making and drive positive outcomes.


Hands On Training




Live Projects


Lifetime Course Material

About certificate

Modulation Digital is recognized for its quality and has gained accreditation for many of its programs. modulation digital certificates are widely accepted by governments, corporate organizations, and higher education, institutes for jobs in higher education, future prospectus.

This certification focuses on skills related to and Excel, SQL (Structured Query Language), Python, for data analysis and Science.

Importance: Recognized by employers using Microsoft technologies, it demonstrates proficiency in tools commonly used for data analysis. Details: Offered by Google on platforms like Coursera, it covers foundational data analytics skills using, SQL, Excel, Sql, Python other tools.

Skill Validation: Certifications validate your skills and knowledge in specific tools, languages, and methodologies relevant to data analysis.

Industry Recognition: Certifications from reputable organizations or vendors are often recognized by employers, showcasing your proficiency in industry-standard tools and techniques.

Learning Path: Pursuing a certification often follows a structured learning path, ensuring that you cover essential concepts and gain practical experience.

Professional Network: Certification programs often provide opportunities to connect with other professionals in the field, expanding your professional network. While certifications can be valuable, it's essential to complement them with practical experience and a strong understanding of fundamental concepts. Additionally, staying updated with industry trends and continuous learning is crucial in the dynamic field of Data Analysis.

Features: Qualified Faculty & Research, State-of-the Art-Infrastructure, Data Sets and Database, Professional Development Opportunities, carrier placement support, Continue improvement Feedback, Ethical Consideration & Responsible AI.

What technology we are using

  •   Introduction to MS Excel
  •   Industry uses of Excel
  •    Advantages of Excel
  •   Features of Excel
  •   Importing Data into Excel
  •   Data Formatting in Excel
  •   Data Validation in Excel
  •   Business Calculations using Excel
  •   Sorting,Filtering,and other Data operation
  •    Pivot Tables
  •   Working with Formulae in Excel
  •   Charts and Graphs in Excel
  •   Macros in Excel
  •   Keyboards in Excel
  •   Text Functions
  •   Statistical Functions
  •   Date and Time Functions
  •   Mathematical Function
  •    SQL and it’s real-life applications
  •    RDBMS concepts
  •   Data Definition Language Statements
  •   Data Query Language Statements
  •   Aggregate Functions
  •   Practical implementation of SQL queries
  •   Use of Date functions
  •   Joins in SQL
  •   What are SQL Views
  •   Implementation of Views
  •   What are Sub-Queries / Nested -Queries
  •   Intro To Python
  •   Python Objects
  •   Python operators
  •   Python Functions
  •   Pandas
  •   Numpy
  •   Data Frame Manipulation
  •   Visualization

Why Choose Modulation Digital For Full Web Designing Course?

Strength your foundational Knowledge and apply this understanding to specific problems, get versatile training in data-sentric research, and build competencies who work in multiple programming in environment.

Learn From a variety of Activities and through high degree of Communication Between Faculty and Students.

Engage and learn at your own faq. Explore our extensive e-libraries, access your learning materials online anytime, anywhere attend live and Recorded lectures and talks that works for your schedule and engage in discussion on our platforms. access study material even on low bandwidth internet on an advanced digital platform that can be used on your phone, laptop, or desktop.

Gain real world insights from industry focussed modules and projects. learn from faculty who come with decodes of rich industry experience and expertise and interact with mentress & faculty to understand industry specific challenges and Oppurtunities.

What Technology We Are Using For Our Valued Customers

Node Js


My Sql




What Saying Our Learners! For Web Designing

Top Companies Hiring Web Designing in India

Why Choose Us

Any Emergency

  • Qualifed Faculty & Research
  • State of-the Art-Infrastructure
  • Data sets and Database
  • Professional Development Opportunities
  • Carrier placement support
  • Continue improvement Feedback
  • Ethical Consideration & Responsible AI

CALL US 24/7 For Demo Class


Have any idea or project for in your mind call us or schedule a appointment. Our representative will reply you shortly
Let's Talk
Web Designiner & Developer

Rahul Kumar

Podcasting operational change management inside of workflows to establish a framework. Taking seamless key performance indicators offline to maximise the long tail. Keeping your eye on the ball while performing a deep dive.

Biography Our Traner

Bring to the table win-win survival strategies to ensure proactive domination. At the end of the day, going forward, a new normal that has evolved from generation X is on the runway heading towards a streamlined cloud solution. User generated content in real-time will have multiple touchpoints for offshoring. Capitalize on low hanging fruit to identify a ballpark value added activity to beta test. Override the digital divide with additional clickthroughs from DevOps data analytics communication.

User generated content in real-time will have multiple touchpoints for offshoring. Capitalize on low hanging fruit to identify a ballpark value added activity to beta test. Override the digital divide with additional clickthroughs from DevOps. Languages realizes why a new common language one could refuse to pay expensive translators.

Our Traner Professional Skills

Software Development

Graphic Design


Web Development

Cyber Security


Other Relevant Courses

Bring to the table win-win survival strategies to dotted proactive domination. At the end of the going forward, a new normal that has evolved generation.

Data Anaylatics Course- FAQs

data analysts gather, clean, and study data to help guide business decisions. If you’re considering a career in this in-demand field, here's one path to getting started.

  •   Get a foundational education.
  •   Build your technical skills.
  •   Work on projects with real data.
  •   Develop a portfolio of your work.
  •   Practice presenting your findings.
  •   Get an entry-level data analyst job.
  •   Consider certification or an advanced degree.

You can find data analytics jobs in all sorts of industries, and there’s more than one path toward securing your first job in this high-demand field. Whether you’re just getting started in the professional world or pivoting to a new career, here are some steps toward becoming a data analyst.

If you’re new to the world of data analysis, you’ll want to start by developing some foundational knowledge in the field. Getting a broad overview of data analytics can help you decide whether this career is a good fit while equipping you with job-ready skills.

It used to be that most entry-level data analyst positions required a bachelor’s degree. While many positions still require a degree, that’s beginning to change. While you can develop foundational knowledge and enhance your resume with a degree in math, computer science, or another related field, you can also learn what you need through alternative programs, like professional certificate programs.

    Getting a job in data analysis typically requires having a set of specific technical skills. Whether you’re learning through a degree program, professional certificate, or on your own, these are some essential skills you’ll likely need to get hired.

    •   Statistics
    •   Python programming
    •   SQL (Structured Query Language)
    •   Data visualization
    •   Data cleaning and preparation.
  •   Handsome salary
  •   high demand
  •   multiple job opportunities
  •   reputed career
  •   freedom to work

Tata fines required after graduation year graduation can be from any subject

On an embrace 4-year-old three years complete graduation and after that six months to get into a job start earning.

Some of the top skills required to become a data Data analyst.

  •    Basic Excel
  •   Basic Python
  •    Basic SQL(Structured Query Language)

Some of the top skills required to become a data science.

  •   Advanced Python
  •   machine learning
  •   Statically learning

The United States of America pays the highest amount of salary to data science.

Each interview questions include a brief explanation of the key concepts and skills that it tests as well as is tips on how to approach and answer the question. By familiarizing yourself with the question and practicing your response.