Overview of Analytics Course in Vadodara

Vadodara is an emerging hub of education in Gujarat, with various universities and colleges offering diverse courses. In recent years, the city has witnessed an increased demand for analytics courses. Analytics is a rapidly growing field that deals with the collection, analysis, and interpretation of data to aid business decision-making. Analytics courses in Vadodara are designed to equip students with the skills and knowledge necessary to work with large data sets and extract actionable insights. They cover a wide range of topics such as data collection, data cleansing, hypothesis testing, regression analysis, and machine learning. Students also learn to use various tools and software like Excel, Python, R, SAS, and Tableau. One of the top institutes for analytics courses in Vadodara is the Parul University. The university offers a Master of Science in Data Analytics (MSDA) program that is a two-year postgraduate course. The course curriculum includes topics like statistical inference, data warehousing, database management, data visualization, and predictive analytics. The course also offers hands-on training with real-world datasets, which helps students acquire practical skills. Another prominent institute for analytics courses in Vadodara is the MS University. The university offers a postgraduate diploma in business analytics (PGDBA) program that is a one-year course. The program aims to provide students with an understanding of analytics and its application in business decision-making. The course covers topics such as data preprocessing, data visualization, machine learning, and big data analytics. Additionally, the university offers a certification course in business analytics, which is a six-month course. The CII School of Analytics is another leading institute in Vadodara that provides analytics courses. The institute offers a diploma in advanced analytics program, which is a one-year course. The course focuses on data science, statistics, and analytics. It covers topics such as data mining, machine learning, big data analytics, and data visualization. Students also get hands-on training on software tools like R and Python. Apart from these institutes, there are various private coaching centers and online courses that provide analytics training in Vadodara. These courses offer the flexibility of learning at one's own pace and provide students with a comprehensive understanding of analytics. In conclusion, analytics courses in Vadodara are in high demand due to the growing importance of data analysis in businesses and industries. These courses provide students with an opportunity to learn practical and theoretical skills that are essential to succeed in the field of analytics. Institutes like Parul University, MS University, and CII School of Analytics offer quality education and excellent placement opportunities, making them the go-to institutions for analytics courses in Vadodara.
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Skill Level

NA

Internship

NA

Live Project

NA

Certificate

NA

Live Training

NA

Career Assistance

NA

Expiry Period

Lifetime
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Skillfloor Course Training Process
Skillfloor Course Training Process

Internship Certificate

internship certificate

Completing this Analytics Course certifies your proficiency in data analysis, statistical methods, and essential analytics tools. This certification shows you can transform raw data into actionable insights, improving decision-making in various business contexts. It indicates your understanding of data visualization, predictive modelling, and data-driven strategy development, providing you with the skills to excel in analytics. This credential reflects your dedication to continuous learning and commitment to leveraging data for strategic advantage.

Analytics Tools Covered

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Why Choose SKILLFLOOR for Analytics in Vadodara?

Why Course Training in Skillfloor

Syllabus

- Overview of data analysis and its importance in business

- Types of analytics: Descriptive, Predictive, Prescriptive

- Role of data in decision-making processes

- Introduction to common tools: Tableau, PowerBI, Excel

- Ethical considerations in data collection and analysis

- Data sources: Primary and secondary data

- Data collection methods (surveys, web scraping, databases)

- Data cleaning techniques (handling missing values, outliers)

- Data transformation and feature engineering

- Data storage concepts (structured vs. unstructured data)

- Descriptive statistics: Mean, median, mode

- Data visualization basics (histograms, scatter plots)

- Identifying data patterns and trends

- Outlier detection and handling methods

- Correlation and causation analysis

- Inferential statistics and probability theory

- Hypothesis testing (t-tests, chi-square tests, ANOVA)

- Measures of central tendency and variability

- Confidence intervals and margin of error

- Regression analysis: Linear and logistic regression

- Principles of effective data visualization

- Types of charts and their uses (bar, line, pie, heatmaps)

- Designing dashboards for different audiences

- Interactive visualization techniques

- Data storytelling for impactful presentations

- Time series analysis and forecasting methods

- Clustering and segmentation analysis

- Decision trees and classification techniques

- Introduction to machine learning in business analytics

- Model evaluation and selection

- Basics of SQL for data manipulation

- Creating databases and relationships

- Aggregating data with SQL (GROUP BY, JOIN)

- Data modeling for business intelligence (star and snowflake schemas)

- Case study: Building a business model with SQL

- Connecting and preparing data in Tableau

- Creating basic visualizations (charts, maps)

- Advanced Tableau functions (LOD calculations, table calculations)

- Building interactive dashboards and stories

- Publishing and sharing visualizations on Tableau Server/Online

- Introduction to PowerBI workspace and components

- Data import and transformation with Power Query

- Data modeling and relationships in PowerBI

- Creating and customizing visualizations

- Publishing and collaborating on PowerBI Service

- Selecting a real-world dataset for analysis

- Defining business questions and objectives

- Conducting data analysis and visualization

- Presenting findings in a comprehensive dashboard

- Peer review and feedback on project

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