We can refer to this link

https://online.hbs.edu/blog/post/data-driven-decision-making

• descriptive

• diagnostic

• prescriptive

• predictive.

refer to the link below:

https://doi.org/10.1016/j.eswa.2024.124101

• Data analytics is in high demand, but job requirements vary.

• Many job postings list unclear or unrealistic expectations.

• AI is changing skill requirements

Problem:

• Uncertainty about needed qualifications (degree vs. experience vs. certifications).

• Skill gaps between education and industry demands.

• Differences in job expectations across industries.

Methodology

• Review job postings

  • Make comparison between roles in different industries

  • Check skills and salary trends

• Research design

• Data collection methods - surveys, experiments, observations, focus group, secondary data

• Sampling techniques

• Quantitative Analysis

• Qualitative Interviews

- Case study approach:

• Case Selection

• Data Collection

• Analysis

• theoretical advancement?

• bridging the gap between employer and employee?

• addressing the remedies for the existing problems in analytics field?

• social and economic impact?

• Job postings analysis

• Surveys

• Interviews

• analysis method: SWOT, case modelling, user stories.

  
  

+) Learn advanced techniques like machine learning, deep learning,...

+) Improve programming skill, especially is C#, Python, R, SQL,...

Practical Insights for Job Seekers: Provide a structured roadmap for aspiring data analysts to acquire the most in-demand skills.

Guidance for Educators: Help universities and training programs update their curricula to better match industry demands.

Support for Employers: Offer insights to recruiters on the essential skills to prioritize when hiring for data analytics roles.

Future Research Directions: Suggest areas for further study, such as how job requirements evolve over time with technological advancements.

Time-series analysis of a business after all data analysts learn a new skill

Data collection methods about the most important skill

-Help job seekers match their skills with what companies need.

-Suggest ways for companies to improve their hiring criteria based on market demand.

• data manipulation

• quantitative analysis-

• data collection

• data visualisation

• data structure and algorithms-

• financial knowledge

• How has the rapid evolution of technology contributed to the explosion of data in recent years?

• Why are data analytics professionals considered pivotal to organizational success?

• Identify common requirements for data analytics roles.

• Data Collection: Scrape job postings from leading job portals such as LinkedIn, Indeed, and Glassdoor over a period of three months.

• Variables Analyzed:

  • Required technical skills (e.g., Python, SQL, Tableau)

  • Preferred educational background (e.g., degrees, certifications)

  • Years of experience required for different levels (entry, mid, senior)

  • Common soft skills (e.g., communication, problem-solving)

• Data Processing:

  • Text mining and natural language processing (NLP) will be used to categorize skills and job expectations.

  • Frequency analysis will identify the most in-demand qualifications.

• Expected Outcome: A ranked list of the most sought-after skills, qualifications, and experience levels.

1. Data Surge: Highlight how today's vast data landscape transforms decision-making from instinctual to analytical.

2. Traditional to Modern: Present data-driven decision-making as a pivotal shift from traditional practices to evidence-based strategies.

3. Competitive Edge: Stress that leveraging data analytics is crucial for businesses to gain an advantage over competitors.

4. Tech Empowerment: Note that advancements in big data and AI enable organizations to derive actionable insights for better decisions.

The key skills for a data analyst in the demanding task

• The study will use a mixed-methods approach, combining quantitative analysis of job postings and qualitative insights from industry professionals.

2. Data Collection Methods

a) Job Posting Analysis

• Collect job listings from platforms like LinkedIn, Indeed,...over a specific timeframe (e.g., last 6 months).

• Extract key information such as required skills, tools (Python, SQL, Tableau, etc.), educational background, and experience levels.

b) Surveys & Interviews

• Conduct surveys with data analysts, hiring managers, and recruiters.

• Perform interviews with industry experts to gain insights on job market trends and skill expectations.

3. Data Analysis Techniques

• Quantitative Analysis:

  • Use techniques to analyze job descriptions for keyword trends.

  • Apply statistical analysis (e.g., frequency distribution, correlation) to identify the most in-demand skills.

• Qualitative Analysis:

  • Thematic analysis of interview transcripts to identify recurring themes in hiring expectations.

II. Core Skills and Knowledge

• Technical Skills:

  • Programming:

    • "Python for Data Analysis" by Wes McKinney (Focuses on Python libraries like pandas and NumPy)    

  • Statistical Analysis:

    • "An Introduction to Statistical Learning" by Gareth James et al. (Provides a good foundation in statistical learning methods)  

  • Data Visualization:

    • "The Visual Display of Quantitative Information" by Edward Tufte (A classic on data visualization principles)  

    • "Storytelling with Data" by Cole Nussbaumer Knaflic (Focuses on effective communication through data visualization)