Data Analytics Course in Noida - Fee, Certification, Placement Assistance

What will you learn?

Python Basics for Data Analytics: Understand Python fundamentals, including syntax, data types, and control structures tailored for data analysis.
Data Manipulation with Pandas: Gain skills in using the Pandas library for data cleaning, manipulation, and analysis.
Data Visualization with Matplotlib and Seaborn: Learn to create insightful visualizations using Matplotlib and Seaborn libraries.
Statistical Analysis and Hypothesis Testing: Explore statistical methods and hypothesis testing to derive meaningful insights from data.
Machine Learning with Scikit-Learn: Master the application of machine learning algorithms using Scikit-Learn for predictive analytics.

Skill you will gain

Data Cleaning and Preprocessing
Exploratory Data Analysis (EDA)
Statistical Analysis and Hypothesis Testing
Data Mining and Pattern Recognition

Explore Modules of this course

Module 1: Introduction to Data Analytics

Understanding the fundamentals of Data Analytics and its applications. Overview of Python as a tool for data analysis.

Module 2: Python Basics for Data Analytics

Learning the basics of Python programming relevant to data analysis. Data types, variables, loops, and functions.

Module 3: Data Cleaning and Preprocessing

Techniques for cleaning and preparing data for analysis. Handling missing values, outliers, and data transformations.

Module 4: Exploratory Data Analysis (EDA)

Visualizing and summarizing data to extract insights. Identifying patterns, trends, and relationships.

Module 5: Statistical Analysis with Python

Applying statistical techniques for inference and hypothesis testing. Descriptive statistics, hypothesis tests, and correlation analysis.

Module 6: Data Visualization and Presentation

Creating informative and visually appealing plots and charts. Using libraries like Matplotlib and Seaborn.

Module 7: Predictive Modeling and Machine Learning

Building predictive models using machine learning algorithms. Regression, classification, and model evaluation techniques.

Module 8: Time Series Analysis

Analyzing time-dependent data for forecasting and trend identification. Techniques like moving averages and ARIMA models.

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At DUCAT, we are committed to providing inclusive educational opportunities to a diverse range of learners. While the specific prerequisites may vary depending on the course, the following eligibility criteria generally apply to all our training courses:

Age Requirement: There is no minimum age requirements, however, some courses may have specific age requirements, which will be clearly mentioned in their respective descriptions.

Educational Background: Most courses are open to all; advanced courses may have prerequisites, as specified in the course details.

Language Proficiency: Proficiency in English is recommended for an optimal learning experience. Course-specific language requirements may apply; consult course details.

Technical Requirements: A reliable internet-connected computer or mobile device is essential. Specific software and hardware requirements are detailed in course descriptions.

Commitment to Learning: Active participation, completion of assignments, and discussion involvement are vital for course success.

Payment and Fees: Some courses may have associated fees or tuition costs. Please check the individual course details for information on fees, scholarships, and payment options.

Please note that individual courses may have additional or specific eligibility criteria based on their content and objectives.

At DUCAT, we believe that education should be accessible to everyone, and we strive to accommodate a wide range of learners.

If you have any questions or need further clarification regarding eligibility criteria for a particular course, our support team is available to assist you.

Online Training : Online training is the most convenient option, as you can enrol from anywhere in the world.

Offline Training : Offline training is the traditional option, where you visit the training centre in person. Corporate enrollment is available for companies that want to enrol their employees in the course.

Hybrid Training : Hybrid training is best as it is as per student comfort where students can take training in both online or offline mode.

Q: What is Data Analytics and How is it Different from Data Science?

A: Data Analytics involves examining raw data to draw conclusions about the information it contains. It focuses on descriptive and diagnostic analysis to answer specific business questions. Data Science, on the other hand, is a broader field that includes predictive modelling, machine learning, and the creation of algorithms to extract insights from data.

Q: Why is Python the Preferred Language for Data Analytics?

A: Python is widely used in Data Analytics due to its simplicity, readability, and a rich ecosystem of libraries (e.g., Pandas, NumPy, Matplotlib) that make data manipulation and visualization easy. Its versatility and the availability of powerful analytical libraries make it a preferred choice for data analysts.

Q: What are Some Common Techniques Used in Data Analytics Using Python?

A: Common techniques in Data Analytics using Python include data cleaning and preprocessing, exploratory data analysis (EDA), statistical analysis, data visualization, and creating visual dashboards for insights.

Q: How Can I Get Started with Data Analytics Using Python?

A: To get started with Data Analytics using Python, you can follow these steps:

Learn Python for data analytics basics and data manipulation with libraries like Pandas.
Study statistics and exploratory data analysis (EDA) techniques.
Familiarize yourself with data visualization tools like Matplotlib and Seaborn.

Q: What Are Some Practical Applications of Data Analytics Using Python?

A: Data Analytics using Python finds applications in various fields, including:

Business Intelligence: Analyzing sales data, customer behaviour, and market trends.
Healthcare: Analyzing patient data, and optimizing healthcare operations.
Finance: Financial forecasting, risk assessment, fraud detection.
Marketing: Customer segmentation, campaign performance analysis.

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