Data Analyst II

A data analyst's main responsibilities include collecting, processing, and analyzing large datasets to extract meaningful insights. They create reports, use statistical methods to identify trends and communicate findings to help organizations make informed decisions. Additionally, data analysts often contribute to data quality assurance and play a key role in optimizing data-related processes.

Major Responsibilities:
Developing and maintaining databases, and data systems reorganizing data in a readable format.
Performing analysis to assess the quality and meaning of data.
Filter Data by reviewing reports and performance indicators to identify and correct code problems.
Using statistical tools to identify, analyze, and interpret patterns and trends in complex data sets could be helpful for the diagnosis and prediction
Assigning numerical value to essential business functions so that business performance can be assessed and compared over periods of time.
Analyzing local, national, and global trends that impact both the organization and the industry.
Preparing reports for the management stating trends, patterns, and predictions using relevant data.
Working with programmers, engineers, and management heads to identify process improvement opportunities, propose system modifications, and devise data governance strategies.
Preparing final analysis reports for the stakeholders to understand the data-analysis steps, enabling them to make important decisions based on various facts and trends.
Scrutinize data to recognize and identify patterns.
Use data modeling techniques to summarize the overall features of data analysis.
Using automated tools to extract data from primary and secondary sources.
Removing corrupted data and fixing coding errors and related problems.

Essential Data Analyst Skills:
Need to be a mix of technical, analytical, and soft skills to effectively analyze data and communicate their findings.

Here are some skills for Data Analysts (not required, but preferred):
1. Data Cleaning and Preparation: Should know how to clean and prepare data for analysis. This includes removing errors, identifying outliers, and transforming data into a format that can be analyzed.

2. Data Analysis and Exploration: Need to be able to analyze data and explore it for insights. This includes using statistical methods to test hypotheses, identify trends, and make predictions.

3. Statistical Analysis: Understanding statistical tests and tools is crucial. Familiarity with mean, median, variance, standard deviation, correlation, regression, and hypothesis testing can be fundamental.

4. Database Management: The ability to query databases is essential for extracting data. Knowledge of database systems.

5. Creating Dashboards and Reports: Need to be able to create dashboards and reports that communicate insights to stakeholders. This includes using tools like Power BI, and Microsoft Word to create interactive dashboards and reports.

6. Data Visualization: Using tools and libraries like Tableau, Power BI, Matplotlib, Seaborn, etc. to represent data in a visual format that's easy to understand.

7. Excel: Often overlooked, but it's still widely used for data analysis and visualization, especially in smaller datasets or in business settings.

8. Critical Thinking: The ability to approach problems logically and make informed decisions based on the data.

9. Attention to Detail: Ensuring accuracy in data analysis and recognizing anomalies or errors in data.

10. Communication: Clearly conveying findings, both written and oral, to non-technical stakeholders. This includes creating reports and presentations that give insights from the data.

11. Problem-solving: Producing solutions to business problems using data-driven approaches.

12. Teamwork: Collaborating with other departments or teams, understanding their needs, and providing them with relevant data insights.

13. Ethical Judgement: Recognizing the ethical implications of data usage, storage, and analysis, especially in terms of privacy

14. Analytical: Strong analytical skills with the ability to collect, organize, analyze, and disseminate significant amounts of information with attention to detail and accuracy


Some Qualifications:
Proven working experience as a Data Analyst or Business Data Analyst
Technical expertise in data models, database design development, data mining, and segmentation techniques
Strong knowledge of and experience with reporting packages (Business Objects etc), databases (SQL, etc.), etc.
Knowledge of statistics and experience using statistical packages for analyzing datasets (Excel, SPSS, SAS, etc.).
Adept at queries, report writing, and presenting findings.
BS in Mathematics, Economics, Computer Science, Information Management or Statistics