Overview of Analytics Course in Guntur

Guntur, a city located in the state of Andhra Pradesh, has become a hub for education in recent years. With the rise in demand for analytics professionals, various top-tier institutes in the city have introduced analytics courses to cater to the industry's needs. An analytics course in Guntur provides students with a comprehensive understanding of various analytics tools and techniques used in the industry. The course curriculum covers a broad range of topics such as statistical modeling, data mining, machine learning, and predictive analytics. The course not only provides theoretical knowledge but also practical exposure to various software tools like R, Python, SAS, and Tableau. One of the primary advantages of pursuing an analytics course in Guntur is the abundance of job opportunities that it provides. The city has a thriving industrial sector with companies such as HCL, Infosys, Wipro, and Tech Mahindra setting up their operations in the city. These companies require a large workforce with analytics skills, and there is a significant shortage of such professionals. Therefore, students who complete an analytics course from a reputed institute in Guntur have a higher likelihood of securing a job in the industry. The analytics course in Guntur is taught by experienced faculty members who have years of experience in the field. They bring their practical knowledge to the classroom, making the learning experience more engaging. The faculties also provide career guidance to the students, helping them create a roadmap towards achieving their career goals. The analytics course in Guntur also provides students with an opportunity to work on real-world projects. These projects are industry-relevant and give students a chance to apply their theoretical knowledge to solve practical problems. Working on such projects helps students build their portfolio and provides them with a competitive edge in the job market. Apart from the practical exposure, students pursuing an analytics course in Guntur also get access to various workshops and seminars. These events provide students with an opportunity to interact with professionals from the industry, learn about new trends and technologies, and network with their peers. Attending such events also helps students stay up-to-date with the latest industry developments. In conclusion, pursuing an analytics course in Guntur can be a lucrative career choice for any individual looking to enter the analytics field. The city's thriving industrial sector, experienced faculty members, and practical exposure make it an excellent destination for students to start their analytics career. Additionally, the course also provides students with an opportunity to develop a strong network, stay up-to-date with industry developments and build a strong career roadmap.

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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 Guntur?

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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