3. Data Analytics - Data, Data, Everywhere - Week 3

Definition:

Database // a collection of data stored in a computer system

Stakeholders // people who invested time and resources into a project and are interested in the outcome

Formula // set of instructions that perform a specific calculation using the data in a spreadsheet

Function // a preset command that automatically performs a specific process or task using the data in a spreadsheet

Query Language // computer programming language that allows you to retrieve and manipulate data from a database. EX: SQL (Structured Query Language)


Data Analysis Tools:

    - Spreadsheets; digital worksheet that stores, organize and sorts data. Google Sheets and Excel

    - Databases

    - Query Languages; SQL

    - Visualization Software; Tableau and Looker


Data Lifecycle:

    - Plan; what data is needed and who is responsible for collecting, storing, sharing data

    - Capture; when data is collected from different sources (outside or inside resources)

    - Manage; how to store, secure, and maintain properly

    - Analyze; data is use to solve problems, make decisions, support business goals

    - Archive; storing data where it is available but may not be used again

    - Destroy; using erasure software or shred physical data


Data Analysis Process:

    1. Ask; define the problem to be solved, the purpose, and understand stakeholder's expectations

            Look at current state and identify how it's different from the ideal state.

            Determine who stakeholders are. Manager? Sponsor? Sales partner?

    2. Prepare; what type of data we need to answer questions, brand new or old data, collect or dont.

            (quantitative data vs qualitative data)

            (cross sectional or points in time or longitudinal over a long period of time)

    3. Process; eliminate errors and inaccuracies that can affect the result.

            Cleaning data and transforming it into a more useful format.

            Removing outliers or combining datasets.

    4. Analyze; using tools to transform and organize the data.

            Use data to draw conclusions, predictions, and drive informed decision-making.

            Do it objective and unbiased way.

    5. Share; interpret results and share to others in effective ways

            Visualizations is important.

            R Language for data manipulation, calculation, and visualization.

    6. Act; business takes all insights from data analysts to solve business problems


Maintaining Data Requires:

    - Integrity

    - Credibility

    - Privacy


Additional Resources:

https://www.fws.gov/data/life-cycle

https://www.usgs.gov/data-management/data-lifecycle

https://sfmagazine.com/post-entry/july-2018-the-data-life-cycle/

https://online.hbs.edu/blog/post/data-life-cycle

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