Overview of Analytics Course in Tirupati

Tirupati, located in the Chittoor district of Andhra Pradesh, is a city known for its religious significance and the famous Venkateswara Temple. However, Tirupati is also home to several prestigious educational institutions, including the Sri Venkateswara University (SVU), which offers numerous courses in various fields of study. One such course that has gained popularity in recent years is the Analytics Course. An Analytics Course focuses on teaching individuals how to extract insights from data using advanced statistical and computational techniques. This course has gained significant importance in the business world as companies now collect vast amounts of data, and it is imperative to analyze this data to make informed decisions. SVU offers an Analytics Course that covers various aspects of analytics, including data mining, machine learning, predictive analytics, and cloud-based analytics. The course is designed to cater to the needs of both professionals and students, as the curriculum covers a broad range of topics, from basic statistics to advanced machine learning algorithms. The Analytics Course at SVU is delivered through a combination of classroom lectures and hands-on practical sessions. There is also a research component to the course, providing students with an opportunity to work on various projects related to analytics. The course is taught by experienced faculty with extensive knowledge in the field of analytics. The Analytics Course at SVU is a two-year program, with four semesters. The first two semesters cover the basics of analytics, including data collection, data preparation, and basic statistical analysis. The third semester focuses on advanced analytical techniques, such as machine learning and predictive analytics. The final semester is devoted to a research project, giving students the opportunity to apply their knowledge and skills to real-world problems. The Analytics Course at SVU has numerous benefits for students and professionals alike. For students, the course offers a chance to develop highly sought-after skills in the job market, which can lead to better job prospects and higher salaries. For professionals, the course provides an opportunity to upgrade their skills and stay abreast of the latest developments in the field of analytics. Additionally, the Analytics Course at SVU is affordable and accessible to all. The fee structure is reasonable, and scholarships are available for students who meet certain criteria. The course is open to all, irrespective of their academic background, making it accessible to anyone interested in pursuing a career in analytics. In conclusion, the Analytics Course at SVU is an excellent opportunity for individuals to learn the skills required to succeed in the field of analytics. With an emphasis on practical learning and research-based projects, graduates of this program are well-equipped to take on the challenges of the rapidly evolving business world. Overall, the Analytics Course at SVU is an excellent investment for anyone interested in pursuing a career in analytics and data science.

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₹70

₹9

Skill Level

Beginner

Internship

Yes

Live Project

2

Certificate

Yes

Live Training

Yes

Career Assistance

Yes

Expiry Period

1 Months
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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 Tirupati?

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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