Overview of Analytics Course in Khammam

Khammam, a city in the Indian state of Telangana, is becoming a hub for education with the rise of analytics courses. Analytics is a branch of data science that deals with collecting, processing, and analyzing vast amounts of data to extract meaningful insights and inform decisions. One of the most significant advantages of studying analytics in Khammam is the availability of quality courses. Several institutions offer analytics courses, both in traditional classroom settings and online. These courses cover critical topics such as statistics, programming, data visualization, and machine learning. Students can choose the mode of learning that suits them best, making it easier for working professionals to upskill and stay up-to-date with the latest trends. Another advantage of opting for analytics courses in Khammam is the access to resources. The city has excellent libraries and online resources that students can use to enhance their learning. Renowned institutions like National Institute of Technology (NIT) Warangal and Indian Institute of Technology (IIT) Hyderabad are within driving distance from Khammam, providing students with excellent exposure to the latest research in the field. Khammam's strategic location also makes it an attractive destination for analytics students. The city is in close proximity to Hyderabad, one of India's leading IT hubs. Companies in Hyderabad, such as Google, Amazon, and Microsoft, are actively hiring data scientists and analysts, making it easier for Khammam students to find jobs after completing their course. Additionally, the cost of living in Khammam is relatively lower than other metropolitan cities. Students can find affordable accommodation in the city while still having access to quality education. This makes studying analytics in Khammam a viable option for students from various backgrounds and income levels. Moreover, many institutions offering analytics courses in Khammam have tie-ups with industry partners, providing students with opportunities to work on real-world projects. These projects allow students to apply the concepts they learn in class to real-world problems, providing them with hands-on experience that can be invaluable when applying for jobs. Industry tie-ups also give students access to industry experts, who can offer valuable insights into the field and help students make informed career choices. In conclusion, opting for an analytics course in Khammam is a wise decision for students who want to become analytics professionals. The availability of quality courses, access to resources, strategic location, affordability, and industry tie-ups make Khammam an attractive destination for students looking to upskill and improve their career prospects. With the rise of data-driven decision-making across industries, analytics professionals are in high demand, and studying analytics in Khammam can make students well-prepared for this future-ready career.
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Skill Level

NA

Internship

NA

Live Project

NA

Certificate

NA

Live Training

NA

Career Assistance

NA

Expiry Period

Lifetime
Skillfloor  Course highlights Skillfloor  Course highlights
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 Khammam?

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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