Data Analytics Course in Delhi

Data Analytics Course in Delhi

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Join the program and get the opportunity to learn under the guidance of an data analytics course in delhi specialist.

Data Analytics Course in Delhi

Expertise

prof trained

80000+

Professionals Trained
batch image

50+

Industry Expert Trainers
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8 Branches

In NCR
corporate

2500+

Corporate Served

Course Duration

--

Certificate

Yes

Live Project

--

Training Mode

Classroom / Online

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Master Data Analyst Training in Delhi

Why DUCAT is the Best Choice for Data Analytics Training in Delhi

DUCAT, the leading IT Training School, offers an industry-leading Data Analytics Course in Delhi. With over 23 years of excellence, we provide students with a comprehensive curriculum and hands-on experience in the latest tools and technologies, ensuring they excel in their careers as Data Analysts. Our training focuses on advanced skills, real-world projects, and 100% placement support.


Key Features of the Data Analytics Course

Our Data Analytics Training in Delhi covers the following essential topics to ensure a well-rounded understanding:

  • Statistical Analysis: Master the fundamentals of interpreting and analyzing data trends.

  • Data Wrangling and Manipulation: Learn techniques to clean and prepare raw data for meaningful insights.

  • Predictive Modelling: Develop skills in forecasting and predicting future trends using machine learning algorithms.

  • Business Analytics Tools: Gain proficiency in Advanced Excel, Tableau, Power BI, MySQL, and other industry-standard tools.

  • Data Visualization: Present your data findings in compelling, easy-to-...

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Learn The Essential Skills

Learn The Essential Skills

Earn Certificates And Degrees

Earn Certificates And Degrees

Get Ready for The Next Career

Get Ready for The Next Career

Master at Different Areas

Master at Different Areas

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What You Will Learn?

  • Data Cleaning and Preprocessing: Understanding techniques to clean, organize, and prepare raw data for analysis, including handling missing values, outliers, and inconsistencies, ensuring data quality and reliability.
  • Statistical Analysis and Visualization: Learning how to apply statistical methods to analyze data trends, patterns, and correlations, and visualizing insights through graphs, charts, and dashboards for effective communication and decision-making.
  • Machine Learning Algorithms: Exploring various machine learning algorithms such as regression, classification, clustering, and ensemble methods to build predictive models and uncover hidden patterns in data.
  • Big Data Technologies: Familiarizing with tools and technologies for handling large-scale datasets, including distributed computing frameworks like Hadoop and Spark, and databases like SQL and NoSQL for efficient data storage and processing
  • Business Intelligence and Reporting: Developing skills to generate actionable insights from data analysis, creating reports and presentations to convey findings to stakeholders, and leveraging data-driven strategies to drive business growth and innovation.

Skill you will gain

  • Data Visualization

  • Statistical Analysis

  • Machine Learning

  • Data Interpretation

Explore Modules of this course

Introduction to Data Analytics:

What is Data Analytics? Importance and applications of data analytics Data analytics tools and software

Data Fundamentals:

Types of data (structured, unstructured, semi-structured) Data sources and data collection methods Data storage and management

Data Preprocessing:

Data cleaning and data quality Data transformation and normalization Handling missing data

Exploratory Data Analysis (EDA):

Descriptive statistics Data visualization techniques EDA tools and libraries

Statistical Analysis:

Probability and statistics concepts Hypothesis testing Regression analysis

Data Wrangling and Data Transformation:

Data manipulation using SQL Data cleaning techniques Feature engineering

Data Visualization:

Data visualization principles Creating meaningful visualizations

Machine Learning Fundamentals:

Introduction to machine learning Supervised vs. unsupervised learning Model evaluation and metrics

Data Analysis with Python or R:

Introduction to Python or R Libraries for data analysis (e.g., Pandas, NumPy) Working with data in Python or R

Advanced Analytics Techniques:

Clustering and classification Time series analysis Text and sentiment analysis

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