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Udacity Business Analyst nd008 v3 0 0

1. Introduction To The Business Analyst Nanodegree Program-zsJDWf36ea0
7. Projects and Progress-imfQrXLhZ5M
8. How Does Project Submission Work-jCJa_VP6qgg
9. Integrity and Mindset-zCOr3O50gQM
10. How Do I Find Time for My Nanodegree Program-d-VfUw7wNEQ
11. Meet the Careers Team-cuKecPpZ7PM
13. Final Tips-1ZVBvM54hQw
15. 1 Youtube Basics-qUi0WgKa1zY
15. Downloading Files-A3jLS-jlBZI
18. Download Tableau Public-2bXsg6SKHG8
18. Tableau Desktop Download-End96VkLQc4
1. Introducing your first project-V6RWhu6rLB4
2. P0 How Does A Predictive Model Work T (1)-wx3SGwHkjdA
1. PAND L4 01-uag26xhmNmo
2. Lesson Introduction-BouY9scXCU4
3. Introduction to CRISP-DM-CRKn-9gVNBw
4. The Problem Solving Framework-z8zSnSSUeNs
5. Data Understanding-L7ZHh80wXJ4
6. Data Preparation-WOPz1oFqHe4
7. Analysis and Modeling-DnbDV3XG6no
8. Validation-pVyaPBO7sR0
9. Presentation and Visualization-iottZF2MVfI
1. Selecting an Analytical Methodology-bdHtEIHKfAo
2. Aggregation-TUQgSOoYIjk
2. Descriptive--d2We-HAx-g
2. Nd008 Ud976 P1 L2 A02 L Non-Predictive Business Problems-S-rnJ5BChAQ
2. Segmentation Business Problems-QDg048qSR-k
4. Introduction to Predictive Business Problems-vwmTj4goI30
4. predictive problems-r8EOv8M0-x4
5. Data Poor Business Problems-ftNtrbQgFpM
6. Data Rich Business Problems-LkpkL7KmGLA
7. Numeric Non-Numeric Outcomes-cAIPn6YRPc0
9. Introduction To Numeric Models-Mqdn4lHyvrI
10. Introduction To Non-Numeric Models-lC7mxzS4L24
13. Lesson Summary-RrcV-axGxvA
01. Applying What We've Learned to a Business Problem-MUkPuID54BI
02. Approaching the Business Problem-TanVsPDXTAo
04. Data Understanding-UVM_2SvF-hM
05. Applying What We've Learned to a Business Problem-BSvvZ9C05-s
06. Introduction to Linear Regression-ctoCtba2BDY
07. Using Google Sheets To Calculate A Linear Equation-voCJX6VpFvE
08. Linear Regression Validation-TCtDXmvXDUc
08. Validation in Google Sheets-RGti0FEK76U
10. Simple linear regression-1cqgBRB3r_k
11. Introduction To Multiple Linear Regression-0c8ngwQuZpg
12. Multiple Linear Regression-aW4rB0Intd0
13. Multiple Linear Regression With Excel-kShe0b-sK4o
13. r-squared-RiKojRP54vs
14. Multiple Linear Regression Validation-n_F9oWVDF4I
17. Dean - Video 2-cmKPGwXNQg4
21. Building Your First Model In Alteryx-qydCM1OXHEM
22. Running The Model-GNiEDZaBWyU
26. Analysis Summary-tzF7GALld04
27. Summary-1zLN1Hr85UY
2. 2 Formula Tool Errors1-pRaDUUZ6c_I
2. 3 Null Values-a64DlgUSvb0
2. 4 Score Tool Errors1-gqmpyzZi6FY
Practice Projectmediapanda-p1-practice-1
Practice Projectmediapanda-p1-practice-2
Practice Projectmediapanda-p1-practice-3
1. PAND L6 01 V2-obIk9SMh8dk
2. Course Intro-B48y5zlpLt0
3. Intro-RSNarQhEdyw
5. The Structure of Data-o5GP_raVsQY
6. Three Types of Data Structure-EXgEK_QsBUs
8. Sources of Data-nxmficvaGbM
9. 02 L Course Outline T-tgVkivSiSnk
10. Data Sources - File Example-Xnr3Sy2aImk
11. Data Sources File Example -wm9pXmpu-xM
13. Alteryx Practice-ciXCyrHqOlA
14. Data Sources - Databases-UHOSbQParbI
15. Sources of Data - Web Scraping Example 2-woQ4y4jlRjk
15. sources of data web scraping example-KqAvWE1migY
17. sources of data web scraping exercise-uaFF0kowrqc
18. Intro to Data Types-sOOXzVmUTrg
19. Data Types-ucrIvI38b6I
21. Data Types Example in Alteryx-mBD0nS8SznI
23. Data Types Exercise in Alteryx-NlrUy5I8vJs
24. Wrap Up-3qNxAtfAgbE
1. Lesson Introduction-YPPoy2ndmUM
2. Alex - Video 5-0C5Lum2Q8vo
3. Dirty Data-1RNBKnVwclI
4. Examples of Dirty Data-Z7ffLdRsftg
5. Dirty Data - Parsing-D0fl4fI_UQE
6. Dirty Data - Parsing Example In Alteryx-XCa98RTIz2s
7. Parsing a Phone Number-RNzR57mAWQU
9. Dirty Data - Extra Characters-wSSbYMa2ok0
10. Dirty Data - Extra Characters Example-hxWpnWPXtvY
12. Solution-NMYsCsVc5tk
13. Dirty Data - Duplicate Data-3Uj1pcDKTrA
14. Dirty Data - Duplicate Data Example-rMJhafBg4-g
16. Solution-DMLd7HArHUQ
17. Missing Data-umYMCKZd5DI
18. What Does Missing Data Look Like-xk1rh1SDOwE
19. Why Do We Care About Missing Data-xMrC-cjLdag
21. Solution-AqOUJlo4ZYY
22. Effect of Deletion on Model-i9DLAvkiRv8
24. Dealing with Missing Data - Deletion Solution 2-QhFE66AivKw
25. Imputation-UCmN2WbWFvA
26. Ways to Deal with Missing Data-v5x2D9fUugU
28. Ways to Deal with Missing Data-dHjl0FDQSkI
29. Advanced Methods for Dealing with Missing Data-2vzA2NYmQy0
30. Missing Data Factors to Consider-wryP6-zRR2U
31. Introduction To Outliers-lI8vjoykDg8
32. Alex - Video 4-TlqB-HudcEM
33. What is an Outlier-aUKfwkHfgiA
34. Why do we care about Outliers-eOoJGPWcb_M
37. Effect of Outliers Solution-yDeh3waxbCw
38. Identifying Outliers-Ept0oZCNLag
40. Dealing with Outliers-rg844bAspNQ
44. Wrap Up-d87KImRWBgE
1. n008_P2_L3_A01_l_Welcome to the lesson-3i2NHaliUAo
2. Transposing Data-2WvuNDxLuO8
3. Transposing In Alteryx-JXOlDBeVJY4
5. Transposing - Solution-F05eoet9fQo
6. Aggregating Data-zaAr38_fh2M
7. Aggregating Data Example In Alteryx-QcXcYqSRPiI
8. Aggregating Data - Exercise -CVljgzTJ12g
9. Aggregating Data - Solution--4YWTmPe6Mc
10. Cross Tabulation-g7ac1OwyGgw
11. Cross Tabulation - Example-MOqxHEkCWX4
13. Cross Tabulation - Solution-Fb9brszWz5k
14. Wrap Up-LkYRkbOLjDI
1. Lesson Introduction-6PihI4719Lc
2. Unioning Datasets-OE_wZTg0Z2I
3. Union - Example-M7Xx2cq510s
5. Union - Exercise-iopBdAhC1hQ
6. Joining Datasets-_j_nPIa4PYk
7. Joining Datasets - Example-yrfrBJ-KDDI
9. Joining Datasets - Solution-qR-LmZ8ljxg
10. Fuzzy Matching-TQYtHKf93w4
11. Fuzzy Matching Continued-cqb4EeDW-S8
12. Fuzzy Matching - Example-IxAp8PAyoxI
14. Fuzzy Matching - Solution-CXrxx2WPWGw
16. Spatial Blending-9j4rkcJv9EM
17. Spatial Blending - Example-yRRLUbUDyew
18. Spatial Blending Example II-kb8W_axGUJc
20. Spatial Blending - Solution-WBv_JQWPwSs
21. Wrap Up-dIF5qXEb44w
22. Closing Remarks-pgLqWRhXVdE
Lesson 06_Create an Analytical Dataset1. PAND L7 01-29QDgpgDQ6s
2. Choosing Predicto-Rate4lqnd0Y
5. Predicto Non-Duplicate Variables-1xa24k0y7Yk
6. Predicto - Correlation-lk2w341K36E
7. Predicto - Correlation Continued-rDRQzG5pogM
8. Predicto - Correlation-K9mSw4joDQk
9. Correlation Plots in Alteryx-L_6hIzmWrtk
11. Preparing to Model-JKm8A-G7rZw
12. Preparing to Model in Alteryx-ZTfQs3J4ym8
13. Data Preparation Solution - Counting Null Values-vbFxr7AG1tc
15. Data Preparation Solution - Visualizing Data-pu0nPxjs0K0
16. Data Preparation Solution - Dealing with Null Values-3GFoVQIMw1w
17. Preparing to Model Categorical Variables-MVEAZQzPTHo
18. Preparing to Model Categorical Variables in Alteryx-1VqbdkOZf0E
1. Introduction-Z8WNfx9Oq9s
2. Parch Posey Database-JOMI560DgXg
3. Entity Relationship Diagrams-YY2TAJLEINA
6. Why Businesses Choose Databases-j4ey7--h9r8
6. Why Do Analysts Like SQL-uCNOtUht2Xc
7. How Databases Store Data-H0C9z_sRvLE
9. Types Of Statements-vLvJbIz94C4
11. SELECT FROM Statements-urOYuuav4BY
15. LIMIT Statement-cCPHNNhBgpQ
18. ORDER BY Statement-wqj2As31LqI
21. Order By Part II-XQCjREdOqwE
24. WHERE Statements -mN0uTnlXaxg
27. WHERE with Non-Numeric Data-_pLx7MHOyjo
30. Arithmetic Operators-fgcJdiNECxI
34. LIKE Operator-O5z6eWkNip4
37. IN Operator-_JPO7wwX3uA
40. NOT Operator-dSQF87oW8a0
43. AND BETWEEN Operators-nBuDPneWcKY
46. OR Operator-3vLGEuXAAvA
46. OR Statement-DRmkKVhe6-s
1. Introduction to JOINs-YvZ010GU-Ck
2. Why Not Store Everything in One Table-rvY4A6FpS40
2. Why Use Separate Tables-UIQBtpmqYOs
3. Your First JOIN-HkX9fkNRbU8
10. ALIAS-viWHJaxWTvw
13. Motivation for Other JOINs-3qdv1Ojc9Og
14. JOINs-CxuHtd1Daqk
14. Other JOINs-4edRxFmWUEw
18. JOINs and Filtering-aI1kbDDNs4w
1. Introduction to Aggregations-5vRf_Ntoxfw
2. NULLs-WYUkLKn6XCw
3. Data Types and NULLs-RgTcYwKqtYI
4. COUNT-b4FCWAEGmLg
5. COUNT NULLs-ngxgqfFFFLQ
6. SUM-0zUP14PeiXk
9. MIN MAX-1ewVsgWUih8
10. AVG-diqCDztOL64
13. GROUP BY-9vb67TF4WV0
16. GROUP BY Part II-0HQ-TshNNQA
19. DISTINCT-YDJEHkgKORY
22. Having-D4gmN0vnk58
25. DATE Functions I-E7Z6GMFVmIY
26. DATE Functions Part II-UPWkDhW4cLI
29. 11 CASE V2-BInXuTY_FzE
30. CASE Statements and Aggregations-asSXB6iD3z4
1. Introduction-2Y279421n3A
2. Introduction to Subqueries-s8ZJMj4gscY
3. Your First Subquery-cTM1jPYXLoQ
6. Subqueries Part II-jko-RrZd0R8
9. SQL Subquery Video-10pmKmTI_CA
11. Subquery Solution Video-Y6S3S0LsMrw
12. Common Table Expressions-qtEKO7B8bXQ
12. Subqueries Using WITH-IszTmDKyKHI
16. Subquery Conclusion-TUYvx2K9-5k
1. Introduction to Data Cleaning-YTtH3NM2BX0
2. Cleaning with String Functions-y1fduSu7Ovc
5. Cleaning With More Advanced String Functions-E6cK8RbYGEc
8. CONCAT-bCxZnQN28Y4
11. CAST-LbyOq4ofLng
14. COALESCE-86vgu-ECBCQ
17. Data Cleaning Conclusion-KkHqnvD9BWY
1. Introduction to Window Functions-u3qLjP8KMKc
2. Window Functions-gp0RPgkDHsQ
7. Running Totals And Count-rNJwmnzUTxg
10. Aggregates in Window Functions-Dxew5w3VF7k
13. Aliases for Multiple Window Functions-RWe03bULYnM
16. Comparing Row to Previous Row-Z_x5ZJyDZog
19. Introduction to Percentiles-t7SX2ZEdxKA
20. Percentiles-Qro8uvysnys
23. Window Functions Conclusion-2ZdocDMw7D8
1. Introduction to Advanced SQL-i0VaVPIKUks
5. JOINs with Comparison Operators Motivation-ClzbfQyhNro
5. JOINs with Comparison Operators-48AgxPygRuQ
8. Self JOINs-tw_VzEGBOvI
11. UNION 1-APRpwqFpGwI
11. UNION 2-so5zydnbYEg
11. UNION 3-oVGmi4zBOT8
11. UNION Motivation-0eRr2K8lo-I
14. Performance Tuning Motivation-aY4_uYWEuoE
15. More on Performance Tuning-ZK1FvNH10Ag
15. Performance Tuning 1-5mVfYZ_bfRo
16. Performance Tuning 2-arMtEhSoq7E
17. Performance Tuning 3-hIAE8W6x5O8
18. Joining Subqueries-rxy-fE5GeLY
19. Congratulations!-_FPpbuuW-1o
1. Introduction to Data Visualization-MUZXLvBI2sw
2. Why Do We Use Data Visualizations-iiOP4PE46f4
4. Further Motivation-sjGxUKrbKoI
5. Data Types Review-xzZZZCZk5YM
7. Univariate Plots-kgmYLreYB0A
9. Scatter Plots -DvlxZ37O4i8
11. Correlation Coefficients-rL5Bn8Fi-zE
13. Line Plots-GsaBT47pjgQ
14. What is the Question-xQJyObqxg3E
15. What About More Than Two Variables -ufKcdUbLj9c
17. Why Data Dashboards-8ni2lCqAVvQ
21. What's Next-AwpX6HkhL0k
1. Introduction-Q0lZkNF6O0g
2. Lesson Overview-Gg77PqkQkhs
3. Exploratory vs. Explanatory Analysis-wvgBSMks4p8
5. What Makes a Bad Visual-zbvB_9f7bFs
6. What Experts Say About Visual Encodings-98aog0eVcC4
7. Chart Junk-3BTBEYOG2o8
8. Data Ink Ratio-gW2FapuYV4A
9. Design Integrity-y72_fVFtqlY
13. Using Color-6bAedqD3ilw
14. Designing for Color Blindness-k4iTzS7t2U4
15. Shape, Size, and other Tools-fzEliHW3ZLM
16. General Design Tips-Zq-wMwOfQqY
18. Tell A Story-_IdOUEhjVGI
19. Same Data Different Stories-jSSnkz3QT5Y
22. Onwards!-i-ulsdVHhCc
1. What is Tableau-LeCpU8HvVg8
2. Download Tableau Public-2bXsg6SKHG8
2. Tableau Desktop Download-End96VkLQc4
3. How This Lesson Is Structured-xfRtO4aFpv0
6. Connecting To Data-WmsAtqbwRI0
9. Combining Data-7KICenO-lKc
12. What Can You Create In Tableau-gNqIvf5iJA8
13. Worksheets-2xRKvQTRtlk
17. Aggregations-4nGL3y3Nq-0
20. Hierarchies-wl_AM-spH68
23. Marks And Filters-FeYRmZHHu0A
28. Show Me-Jpk99mgmwaA
31. Small Multiples And Dual Axis-bx6MxsoDqsI
36. Groups And Sets-Yb-91NVNgTA
40. Calculated Fields-tR-K9Mvd4B0
43. Table Calculations-VJfCNO0J9jY
47. What's Next-y46uDftUXHo
1. Communicating With Your Data-KDnca1zszIo
2. What's Ahead-ggbCydfI1JM
3. Hierarchies with Trina-ys8Cn0o5gNI
5. Tableau Dashboards Stories with Trina-i9xslfFp80g
9. Extra Practice With Dashboards-Va2zNfnUC6o
12. Congratulations!-sCQ7ZViODaw
1. PAND L9 01-HeIn9MYIHrU
1. Introduction-axcFtHK6If4
2. Job Search Mindset-cBk7bno3KS0
3. Target Your Application to An Employer-X9JBzbrkcvs
4. Open Yourself Up to Opportunity-1OamTNkk1xM
1. Convey Your Skills Concisely-xnQr3ohml9s
2. Effective Resume Components-AiFcaHRGdEA
3. Resume Structure-POM0MqLTj98
4. Describe Your Work Experiences-B1LED4txinI
5. Resume Reflection-8Cj_tCp8mls
6. Resume Review-L3F2BFGYMtI
1. Convey Your Skills Concisely-xnQr3ohml9s
2. Effective Resume Components-AiFcaHRGdEA
3. Resume Structure-POM0MqLTj98
4. Describe Your Work Experiences-B1LED4txinI
5. Resume Reflection-8Cj_tCp8mls
6. Resume Review-L3F2BFGYMtI
1. Convey Your Skills Concisely-xnQr3ohml9s
2. Effective Resume Components-AiFcaHRGdEA
3. Resume Structure-POM0MqLTj98
4. Describe Your Work Experiences-B1LED4txinI
5. Resume Reflection-8Cj_tCp8mls
6. Resume Review-L3F2BFGYMtI
1. Get an Interview with a Cover Letter!-BH1KY63YfAM
2. Purpose-7F7cMCTcyhM
3. Cover Letter Components-DVvLiKedRw4
4. Writing Your Introduction-5S5PH73WLLY
5. Writing the Body-aK9Qnv3a6Wg
6. Write the Conclusion-i3ozyhGPmIg
7. Format-Xlqoq-SoJso
2. Lesson Introduction-NYs17_tPn0k
3. Course Outline-N_6grm-eiOc
5. Classification Examples-9bgOT3QDilE
7. Binary vs Non-Binary - Solution-AupYK4RPYCo
1. Binary Classification Problems-VCB1q2eRlo4
2. Logistic Regression-wwL8TH_5mxw
3. Logistic Regression - Continued-KUl3FWPBfso
4. Logistic Regression - Example-TWOw9ip3p6c
6. Logistic Regression - Solution-3k4ZtnOI_8A
7. Logistic Regression - Stepwise-ukJFwYAt8S8
8. Logistic Regression - Stepwise-MXyVnRktUWE
10. Logistic Regression - Stepwise Quiz-YwNPZz8nbVU
11. Validating Models-06Ee1Mkweqw
12. Logistic Regression - Stepwise Validation-WbFHtdkk0hc
13. Decision Tree Model-78lE5QbY4hc
14. Decision Tree Model-i4oBZqknTQY
15. Decision Tree-BTrg4vNv_qk
16. Decision Tree Results II-yo6WeYUscuM
16. Decision Tree Results-oYG0KgQA4U0
19. Decision Tree Validation-tWX2FzBnrEs
20. Introduction to Model Comparison-9VIwZ9lW-u4
21. Model Comparison-nUAT1vAGw8I
22. Scoring the Model-BR30t_9GPaM
24. Scoring Model-oMuJM14ycRY
26. Outro-Iu6uGg-
1. Non-Binary Classification Problems-_PjcKSH2CIw
2. Decision Tree-2p9LkxdfTGI
4. Decision Tree - Solution-C0-N01qI75U
5. Decision Tree - Validation-i2clxQgFzW4
6. Forest Models I-6yICuCnlh5Q
7. Forest Models Example-FbpLfHNAEI0
8. Build a Forest Model-9fy0O8BadJg
9. Forest Model Results-bWLIVpaW478
10. Build a Forest Model Continued-D4v0Cv9zH4g
13. Solution-eHhKpw9VKCg
14. Forest Model Outro-SJfTdk3GcVQ
16. Boosted Model-kSzEe4PW0Ok
17. Boosted Model - Build Model-10ci9UjMqwc
18. Boosted Model - Results-W-zU2zLG5sY
19. Boosted Model - Observe Results-uQRTcc9YEVw
20. Boosted Model - Validation-G-RT0rVjYcU
21. Boosted Model Outro-Se4o6neLtOQ
22. Model Comparison-J18bHj7aZHc
24. Score the missing data-Qet85gXAiwI
25. Outro-FOXBsdL36C4
2. Welcome to AB Testing-SlvB4u_cs_U
3. Units-LvUAFuFTXmQ
5. Treatment and Control Groups-uf8Y4BgUmTc
6. Experimental and Control Variables-e8XIYDM_O4g
8. Control Variables-BBBWcO1YmE8
9. Testing Correlation-3taL1mYVHYM
10. Lurking Variables-GjOiV8FpviE
11. Experimental Design-VXY4SgfoSlQ
13. Experiment Duration-2jZsJkCfgO0
15. Conclusion-0BVte-RNQLw
1. Intro To Randomized Design-TnRj9FU8iO0
2. Selecting Variables in an Experiment-yyAMpk_tPt0
4. Control Variables-0F2Po6Q4Zkw
5. Experiment Design and Setup-32Nl8YI8UYU
6. Identify the Control Variables-3PJtJwUkTqQ
7. Sample Size-CBLlumsPGYk
8. Preparing the Data for Analysis-nfOrJlDZT7A
9. Analyzing the Results-EOp0KiqWBZY
10. Analyzing Results Example-sZ4FI8HPsXU
13. Analyzing Results in Alteryx-c2eEsW3DOjA
15. Conclusion-eLAYJQD1o5c
1. Introduction to Matched Pair Design-2tu_7QoyY9Y
2. Selecting Treatment Units-ur6FeNnHFMQ
3. Selecting Control Units-yMSzO8GHO7w
6. Selecting One Control Unit for a Treatment Unit-JFu66fhABf8
7. Selecting Multiple Control Units for a Treatment Unit-iQ9YdopPaTQ
8. Matching Stores Example-hwbxJXeJK6A
11. Analyzing the Results Overview-gCK24-8NK9c
13. Analyzing the results with Alteryx-mwWb2Yd8Ktc
14. Interpreting Results-_F2VrombQNw
16. Analyzing Match Pair Design Solution-Yu1TvQrWDm0
17. Conclusion-1nzcoMK5Wj4
1. Introduction-O65MhMq9XFo
2. Pricing Elasticity Analysis Problem-xogIdabm9Pw
4. Select Discrete Control Variables-r4sNsOgZY1w
7. Select Continuous Control Variables-atlzVKhXN-c
9. Run Test-HwMFACVa3nw
10. Filter Calculate Date Fields-ufajUmp_yK4
11. Weekly Store Traffic Data-6TfJrXq-dXY
12. Create Discrete Data Table-vj1Byzs4vXE
13. Store List Data-ckUFJ33ggtc
15. Sales Data-Fzu3n9tF-pc
16. Preparing Control Treatment Units Part 1-bIzeQG3tHZ4
16. Preparing Control Treatment Units Part 2-P-5Vnj6Bpm8
16. Preparing Control Treatment Units Part 3-K_higfJ3DqE
17. Performing the analysis-5LmjqJlVNHU
20. Conclusion-Mig-5mxucPw
1. Why Network-exjEm9Paszk
2. Elevator Pitch-S-nAHPrkQrQ
4. Meet Chris-0ccflD9x5WU
5. Elevator Pitch-0QtgTG49E9I
6. Pitching to a Recruiter-LxAdWaA-qTQ
7. Use Your Elevator Pitch-e-v60ieggSs
1. Welcome to Time Series Forecasting-NN9lbMyXjOA
2. Introduction to Time Series-xvgubPMqtc8
3. The Business Problem-R8Lxt0ooQ9I
5. Simple Forecasting Methods-kIcI43KHFlE
6. Time Series Components-ueMuuaEz1xI
7. Trend-OwW5REl4aFg
9. Seasonality-LnqF6AgJ4_0
10. Seasonality Plot-Rcfw87IqRZo
11. Cyclical Patterns--L-DoRF21Fg
14. Outro-e1jszqylQis
1. Introduction to ETS Models-U-Rw5zCDzKg
2. Time Series Decomposition-EvWNCNOmFS8
3. Identifying Additive or Multiplicative Terms-FMJHhWoVQEQ
6. Simple Exponential Smoothing-NJEBSb12SCE
8. Holt's Linear Trend Method-9ntDzsyQTQI
9. Exponential Trend Method-7VITfY_dgao
10. Damped Trend Method-p1MGASTnB-Y
11. Holt-Winters Seasonal Method-DYkr9lpTlU0
13. Constructing An ETS Model-9ujKCjlEOvY
1. Introduction to ARIMA Models-wSXgmn7D30s
2. Arima Models-MASBEpaQysw
3. Stationarity-CKZXGB48Lvs
5. Differencing-dTLlgJhAyBA
7. Differencing Solution-lXFebDRzLGo
8. Autocorrelation Function Plot-71pFF34hkpk
9. Partial Autocorrelation Function Plot-mHklpFThJDU
10. Autoregressive Component-mEeDn1SoxH0
11. Moving Average Component-DbEO7ujH6Hk
13. Integrated Component-UK2rqClvTpI
14. Seasonal ARIMA Models-aLY6JhkZxCo
15. Seasonal Differencing-sv17IT4FbI4
17. Seasonal AR and MA Terms-vrw57rdi1vI
18. Constucting an ARIMA Model-UIy3N7S26DY
1. Analyzing and Visualizing Forecasting Results-bZNDJ1KNR3Y
2. Holdout Sample-R0pzzeGgqLc
3. Residual Plots-TZ2qfhEUNWc
4. Visualizing Results-ggNLx9EJwCI
5. Calculating Error-C9f7AjiOC3E
8. Akaike Information Criterion-lyh20M6JKQE
9. Choosing the Best Model-XrTSfQEmx7A
10. Confidence Intervals-niqNBmNyKwc
11. Outro-4bSN8nPZtH4
1. Welcome to the Course-YIohCxd-yTE
2. Standardization vs. Localization-e-OsHn5cues
3. Grouping Exercise 1-bOf9rHgi8PM
4. Grouping Exercise 2-i8XGseBDQw0
5. Defining Segmentation-O_0Fke3k_XM
6. Distance in Learning-DJST_spc_Z8
8. Examples For Uses Of Clustering-zvKZiRmz1b0
9. 09 L Unsupervised Learning-fnwQeWZwaqc
10. Business Problem Introduction-rsWVahpNkEI
1. Data Preparation Introduction-1-DNZYmnxO4
2. Getting the Right Data-3q66CIjXY_E
3. Examples Of Selecting Data Based On Objectives--WU2TJu2B54
4. Examples Of Selecting Data Based On Objectives-UdEBQCgGkQ8
5. Predetermined Bias In Transactional Data-Cl3GC6IeerQ
7. Data Types in Clustering-aw5uLwqbp80
8. Data Quality-f_whF6dtLSg
9. Scaling-5XJWDRK3uBw
11. Data Prep Quiz-GudX5P9HVcM
12. Transforming Variable-OIpWZrrFuGs
13. Visualizing The Data-kv4A1eJUngA
14. Lesson Summary--YQQxo4BovU
1. Lesson Introduction-nXbyj6-Sozk
2. Variable Selection And Reduction-H6Z6PjHng0I
3. Variable Reduction Example-E9fqQ0soIZ4
4. Factor Analysis and PCA Continued-i1Iir102a6I
4. Factor Analysis and PCA Overview-dXacZklqpjo
5. PCA Details-qhOIe4VApRY
8. PCA-pHuAnRu08Ek
9. PCA Results-Hd0smDglM_g
10. Visualizing PCA Results-0S-Bm6Li374
12. PCA Results 2-lkglITwlwjI
14. Finishing Off the PCA Data-ZiBJAI-lwmw
15. Lesson Summary-tIxc8E1zouU
1. Intro To Clustering Tecniques-MmHE2_XwfhM
2. Hierarchical Clustering-OKer2AUri3s
3. K-Centroid Clustering-AUCzl6k4QJE
4. Comparison Of The Two Methods-SQ75x-2Bkto
5. How Many Clusters-F7XtWMymP0U
6. Subjectivity In Selecting Number Of Clusters-ypBA2IyF0yA
7. How Many Clusters - Hierarchy-LRAdqAfPI4I
8. How Many Clusters - K-Centroid-hTtsGVFBReg
9. Cluster Validation In Alteryx-FLaIl8ZRtGQ
10. Custer Validation-bwerJs3FeV0
12. Creating The Clusters Using K-Centroid Methods-R_hrRWcXpWU
13. Creating The Cluster Model-G7iQATUVWbY
14. Interpreting Cluster Results-NOVglGU58Dc
15. Applying The Model-sMUwN4tx3EQ
16. Wrap Up-F-NyzjgNKJc
1. Lesson Introduction-5h9g32eZrIs
2. The Iterative Nature Of Clustering-lUW-7gS0Ato
3. External Validation-VcG483nrmIc
4. Validating Through Visualization-lh3debw-EAk
5. Validating Through Visualization II-3rzVIm4OV9M
6. Validating Through Visualization III-XJc0cA6zbeI
7. Communicating The "story" And Ongoing Testing-AsyPGidt4v0
8. Conclusion-Birc4tsABeQ
1. Intro to SQL for Data Analysis-CU4igXr9xTw
2. What's a Database - Intro to Relational Databases-V4gbPVdUOpw
3. Looking at Tables-08GsrEIOP9Q
3. Looking at Tables-e8zSXnyskro
4. Data Types and Meaning-Bb2K_pql26Y
4. Data Types and Meaning-OB7iJnEY0Tg
5. Data Meanings--laXTx9corQ
5. Data Meanings-0-LYyC_70jk
6. Zoo-YG8zfeRXAvM
7. Anatomy of a Table-PhvA7DCgtpw
8. Aggregations-TE9uvPwvpu4
9. Queries and Results-l_HFnHve3Ok
9. Queries and Results-ZFn9xVQ0hD4
10. How Queries Happen-v3EkXogyIL4
11. Favorite Animals-O7kWdC8ffEw
12. Related Tables-lMwSjDYgCq0
13. Uniqueness and Keys-TIayTr4y9RA
14. Primary Key-80aFO4KmcPM
14. Primary Key-q23Nlo1Rfn4
15. Joining Tables-lXoiI0gx3MU
16. Database Concepts-PXNaLWJ5SKg
16. Database Concepts-WZRsqdNrC1A
17. Summary-1Gp4l-ZTCVk
1. SQL is for Elephants-BYAyquwZmdM
2. Talk to the Zoo Database-9AVeGkUcPYQ
2. Talk to the Zoo Database-yLeLxDyXCSY
3. Types in the SQL World-8swA-PqlEP0
5. SELECT WHERE-aznzJPXavDg
5. SELECT WHERE-GUD6yl0A1Aw
6. Comparison Operators-0X7voyhbZ8k
6. Comparison Operators-N5T8ouw1SGc
7. The One Thing SQL is Terrible At-wrjCB1abNyM
8. The Experiment Page--GlHaeB-vic
9. SELECT Clauses-bR7EoKjN0EM
9. SELECT Clauses-_bnZEnWWqXQ
10. Why Do It in the Database-6vfZYhhmuLU
11. Count All the Species-CvB2qkSk1Zc
11. Count All the Species-vfEi3aOawTI
12. INSERT-49B5hlxzTRc
12. INSERT-NRCs22HJ-vI
13. Find the Fish Eaters-AnksTk1zI3Y
13. Find the Fish Eaters-bcwFahJsww0
14. After Aggregating-eeV5K5HKVx4
14. After Aggregating-kIITzFRh5mQ
15. More JOIN Practice-bnwLadF8gKo
15. More JOIN Practice-_7Vqou7k7fI
16. Wrap Up-BGt2DQer6I4
1. Intro to Creating Tables-mIIL-p2gaK4
2. Normalized Design Part One-LQq5F77ANiY
3. Normalized Design Part Two-l6SDnhM7B_k
4. What's Normalized-f1fz9cLnetM
4. What's Normalized-T_LpzJl-EVE
5. CREATE TABLE and Types-7DCw86WMpYo
6. CREATE and DROP databases-3kycKkbQK6A
7. Primary Keys-AaLIKufl_mY
7. Primary Keys-P1NzBPnfl2E
8. Declaring Relationships-1FjqstT1faI
9. Foreign Keys-fnbLMcd0FGQ
9. Foreign Keys-mFPq74OMkKk
10. Self JOINs-dHS0BtLFTSQ
10. Self JOINs-P1aYwQhPnPQ
12. What's a DB-API-06LsRU4pmkA
13. Trying Out DB API-4Ywln5AT6Hc
13. Trying Out DB API-QTq9pGMoZAE
14. Writing Code with DB API-Zd0PAgb6vY4
15. Inserts in DB API-pn3MunHovMc
15. Inserts in DB API-RfYFqGPxMQM
16. Subqueries-YWZ-bgnZWaM
17. One Query, Not Two-n7v-y8UrVJ0
17. One Query, Not Two-_c_chcNiHyo
18. VIEWs-omL8MVZjLts
18. VIEWs-t_ZSrov187k
19. Outro-q7a9w02Uepw
1. Introduction to Data Visualization-MUZXLvBI2sw
3. Anscombe's Quartet-Ftp3mmItV-k
3. Why Even Create Graphics - Data Visualization and D3.js-lqI3LFwMVUc
5. Data Types - Data Visualization and D3.js-ZqRIS9etBqY
7. Visual Encodings - Data Visualization and D3.js-14FJU1kP6-M
8. Rankings of Visual Encodings - Data Visualization and D3.js-ycsXqX7T80Y
1. Introduction-Q0lZkNF6O0g
2. Tipos Comuns de Gr ficos-xD2_AU6atqA
6. Gr ficos Comuns e Menos Comuns no plot.ly-V9xxdiwJtWk
13. Communicating with Color-QnFtYKXf7TQ
16. Gr fico Lixo-jm2qOX2Q2HQ
17. Data Ink Ratio-CJouyPxHb84
20. Onwards!-i-ulsdVHhCc
1. What is Tableau-LeCpU8HvVg8
2. Download Tableau Public-2bXsg6SKHG8
2. Tableau Desktop Download-End96VkLQc4
3. 01 Connecting To Data-zuJPPSlbW2c
4. 02 Joining Tables-vSjKBMgW1Fw
5. 03 Tableau Interface-q3E4CCYeIyM
6. 04 Aggregation Granularity-GuOytlkqdrQ
8. 05 Hierarchies-vkHeQcI08yc
8. Hierarchies with Trina-ys8Cn0o5gNI
9. 06 Practice With Marks And Filters-RPAv7WLkutk
15. Sets and groups with Trina-I59eqlvfMZk
17. 07 Calculated Fields-dXEvq03E5lc
18. 08 Table Calculations-Vkf3Ft93zMc
23. L3 outro-y46uDftUXHo
1. Communicating with your data-KDnca1zszIo
2. Exploring data with Trina-sLzULEjMY1E
3. Digging deeper with Trina-i9xslfFp80g
8. Further Learning-SnLQlP5J7c8