19. Data Analytics - Process Data from Dirty to Clean - Week 5

Job Analyst Job-Application Process and how to make a resume.


Definitions:

Transferable skills // skills and qualities that can transfer from one job or industry to another.

Soft skills // non-technical traits and behaviors that relate to how you work

        Example: detail-oriented, demonstrating perseverance


Job Application Process:

        - Recruiter -> HR Manager -> Job Offer


Tips:

        - Using terms in the industry will show you have knowledge.

        - Gather a lot of information of the job and the company.

        - Resume is a snapshot, to be brief.


Creating a Resume:

        - Contact information at the top:

                - Name, Address, Phone number, Email address

        - Resume that focus on skills and qualifications is good for job gaps or entry positions.

        - Summary paragraph (Optional). 

                - 1-2 Sentences to show strengths.

                - Positive verbs or skills and evidence like timeframes for experience,

                - "Entry-level data analytics professional recently completed the Google Data Analytics

                    Professional Certificate"

        - Work Experience Section (If using work experience format and not skills/qualifications)

                - Can include past/current job positions

                - Can include volunteering positions

                - Describe these roles so that it relates to the job you're applying for. Include wanted

                    qualifications in sentence.

                - Highlight the impact of your role, show the results.

                        - Accomplished [X] as measured by [Y] by doing [Z]

                        - "Selected as one of 275 participants nationwide for this 12-month professional

                            development program for high- achieving talent based on leadership potential

                            and academic success"

        - Education Section

                - Can include online courses.


        - Technical Skills Section

                - Example: SQL, language proficiencies, etc


Writing about experience:

        - Focus on accomplishments first, use XYZ formula.
        
        - Phrase work experience and duties using PAR statements

        - Describe jobs that highlight transferable skills

        - Describe jobs that highlight soft skills


Creating a Resume for Data Analyst:

        - Show that you're a clear communicator. Be direct and coherent.
        
        - Show strong technical skills and abilities and highlight them.

                Example: pattern recognition, strong analytical skills, relational databases and SQL, strong
                data visualization skills, proficiency with spreadsheets, SQL, R, and Tableau.

        Summary Section:
            
                - If transitioning from different career, state it.

                - PAR STATEMENTS, can use everywhere in resume.
                        
                        - Problem, Action, Result

        Skill Section:
        
                - Include any skills gain from courses or through projects.

                - Example: Spreadsheets, SQL, Tableau, R
                
                - Include Section for "Programming Languages": SQL and R

                - Include tasks and features: Creating pivot tables, sorting, filtering, etc.

                - Common professional skills for entry-level data analyst:
                        - SQL (structured Query Language)
                        - Spreadsheets
                        - R/Python Statistical Programming
                        - Data Visualization Tools (Tableau, Microsoft Power BI, etc)


        Work Experience:
        
                - Show transferable skills and how it can be use for job applying to.
        
                - Show place and length of employments

                SOFT SKILLS:

                - Communication skills are important; tells you can describe complex ideas to ordinary
                   people. SHOW OUTCOMES, PAR.

                - Problem Solving: find and troubleshoot problems. Break-down problems using strategies.

                - Teamwork: benefit for the team. can work with people, bring value to company.

                - Presentation Skills, Collaboration, Communication, Research, Problem-solving skills,
                    Adaptability, Attention to detail


Additional Resources:

https://www.youtube.com/watch?v=cBbYhhH399c&list=PLqFaTIg4myu-npFrYu6cO7h7AI6bkcOlL&index=8

https://www.youtube.com/playlist?list=PLqFaTIg4myu-npFrYu6cO7h7AI6bkcOlL

https://www.youtube.com/watch?v=xrhPjE7wHas&list=PLqFaTIg4myu-dNobDHQZPrD2wH27PthCG

https://www.youtube.com/watch?v=X6orAXDIrds&list=PLqFaTIg4myu-dNobDHQZPrD2wH27PthCG&index=5

https://www.youtube.com/watch?v=aXUsrKPTBvY&list=PLqFaTIg4myu-dNobDHQZPrD2wH27PthCG&index=6

https://www.youtube.com/watch?v=0W0Zrc-m5r8&list=PLqFaTIg4myu-dNobDHQZPrD2wH27PthCG&index=2

https://www.youtube.com/watch?v=iP0Fxg4oqUQ&list=PLqFaTIg4myu-dNobDHQZPrD2wH27PthCG&index=8

Comments

Post a Comment

Popular posts from this blog

2. FreeCodeCamp - Dynamic Programming - Learn to Solve Algorithmic Problems & Coding Challenges

Dynamic Programming - Parts      PART 1 - Memoization     P ART 2 - Tabulation Dynamic Programming with Fibonacci Example Recursion: Slow because time complexity is O(2^n) func fibR (x int ) int {     if x <= 2 {         return 1     }     return fibR (x- 1 ) + fibR (x- 2 ) } Dynamic Way Example Recursion with Stored Value:  // Fast because it removes duplicate processes by checking map. // Tail recursion-ish // Results are hitchhiking each recursive call func fibR2 (x int , y map [ int ] int ) int {     if _ , ok := y[x]; ok {         return y[x]     }     if x <= 2 {         return 1     }     y[x] = fibR2 (x- 1 , y) + fibR2 (x- 2 , y)     return y[x] }
Read more

20. Data Analytics - Analyze Data to Answer Questions - Week 1

Goal of analysis is to identify trends and relationships within data so you can accurately answer the questions you're asking. Most data is organized in tables, always have data in the right format. Definition : Analysis // process used to make sense of the data collected Sorting // involves arranging data into a meaningful order to make it easier to understand, analyze, visualize Outliers // data points that are very different from similarly collected data and might not be reliable values Filtering // extracting specific data that meet certain criteria while hiding the rest Customized sort order // when you sort data in a spreadsheet using multiple conditions The 4 phases of analysis:          1. Organize data // getting the right data in the right size and order                     - Sorting : arrange data in meaningful order                     - Filtering : can help find errors or outliers          2. Format and adjust data // filtering or sorting or compiling data in a better
Read more

3. Algorithms - Selection Sort

Selection sort sorts an array by finding the lowest value element and appending it to an empty array. The algorithm will shrink the unsorted array and repeat the process recursively until all unsorted elements are used up. package main func selectionSort (list *[] int ) {     sorted := make ([] int , 0 , len (*list))     unSorted := *list     for i := 0 ; i < len (*list); i++ {         min := unSorted[ 0 ]         index := 0         for i , e := range unSorted {             if e <= min {                 min = e                 index = i             }         }         sorted = append (sorted, (unSorted)[index])         if index == 0 {             unSorted = unSorted[ 1 :]         } else if index == ( len (*list) - 1 ) {             unSorted = unSorted[:index]         } else {             unSorted = append (unSorted[ 0 :index], unSorted[index+ 1 :]...)         }     }     * list = sorted } /*func main() {     x := []int{4, 6, 3, 2, 9, 7, 3, 5}     fmt.Print
Read more