Data Analytics Course In Ghaziabad

Data Analytics Course In Ghaziabad

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

Data Analytics Course In Ghaziabad

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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Best Data Analytics Training in Ghaziabad

Are you looking for a Data Analytics Course in Ghaziabad? Ducat India offers the best Data Analytics Training in Ghaziabad with the best industrial experts. The training will be a completely practical learning approach and live project-based. Ducat provides highly professional and expert training as per the industry needs, with a special focus on 100% practical and project-based programs, hands-on sessions with a specific focus, online doubt-clearing sessions as well as backup online recorded sessions, and a forum accessible for problems and requests.

Ducat is the best Data Analytics Institute in Ghaziabad offers the best Data Analytics Certification course with the Industrial experts who have 10+ years of professional experience. Our training approach concentrates on developing soft skills, logical thinking, personality, and interview preparation with industry experts, along with enhancing technical skills such as Excel, data-driven presentations, SQL data manipulation, Python data analysis, and Tableau data visualization.

Data Analyst Course in Ghaziabad offers classroom training and Data Analytics Online Courses are both available with job-oriented training, including skills and technologies such as statistical analysis, text mining, regression modelling, hypothesis testing, predictive analytics, Tableau, and

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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: You'll learn techniques to clean and preprocess raw data, ensuring its quality and usability for analysis, including handling missing values, outliers, and inconsistencies.
  • Exploratory Data Analysis (EDA): EDA techniques will be taught to uncover patterns, trends, and relationships within datasets through visualization and statistical methods, providing insights that guide further analysis and decision-making.
  • Statistical Analysis: You'll delve into statistical methods such as hypothesis testing, regression analysis, and probability distributions to infer insights and make predictions from data, enabling evidence-based decision-making.
  • Machine Learning: Data analytics often involves applying machine learning algorithms for predictive modeling, classification, clustering, and recommendation systems, equipping you with tools to extract valuable insights and automate decision processes.
  • Data Visualization and Communication: You'll learn how to effectively communicate your findings and insights through compelling data visualizations and storytelling techniques, ensuring that your analysis results are understandable and actionable for stakeholders.

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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