Kupas Tuntas Berkarir di Bidang Data

For those of you who are interested in the data field, let's join the DQLAB x Telkom CODEX Webinar because there will be a discussion about career preparation in the data field. We discussed about three main topics: 1. Data professional organization structure and roles 2. Data scientist use cases 3. Challanges to be data professionals.

Transcript

1. Berkarya Di Bidang Data Di Digital Telco Company Photo by Tomas Sobek on Unsplash.This document created by Iqbal Hanif. Not for sale & distribution without permission from its creator.
2. Hold On! Please Enter This Survey This document created by Iqbal Hanif. Not for sale & distribution without permission from its creator. Use this link: https://www.menti.com/oqr6do8qd6 OR Visit menti.com and enter this code: 13 00 62 For live result, you can use this link: https://www.mentimeter.com/s/82465bf7db0c5408bca789d45c90202a/5f49a92a8578
3. Iqbal Hanif Institut Pertanian Bogor (2011 - 2015) Telkom Indonesia (2016 – now) Officer 2 / Junior Data Scientist (2020) Officer 3 Data Scientist (2017) Trainee - GPTP IV (2016) S1 Statistika, Minor: Ekonomi & Studi Pembangunan This document created by Iqbal Hanif. Not for sale & distribution without permission from its creator.
4. Outline Challenges to be Data Professionals Type of Use Cases Big Data in Digital Telco’s Big Data Analytics Organization Structure Big Data Analytics Products/Services Big Data Analytics Projects Classification Regression Clustering Statistical Analysis Data Visualization Competitions Working Style Apply for a Job Learn The Organization Growing Up Skills Icons made by Flat Icons from www.flaticon.com. This document created by Iqbal Hanif. Not for sale & distribution without permission from its creator.
5. Big Data In Digital Telco Photo by Stephen Dawson on Unsplash.This document created by Iqbal Hanif. Not for sale & distribution without permission from its creator.
6. Big Data Organizations Big Data Platform Big Data Analytics Big Data Managament Membangun analytic as a service Bertanggung jawab mengelola big data platform Bertanggung jawab mengelola fungsi data acquisition, data integration, data mart, dan data mining Mengembangkan kapabilitas dan kapasistas big data platform Melakukan manajemen data quality, data security & governance Membangun model untuk peningkatan kualitas program internal dan eksternal Mengembangkan text, voice, dan video analytics This document created by Iqbal Hanif. Not for sale & distribution without permission from its creator.
7. Big Data Analytics Skill Digital Skill Description Skill Set Data Analyst Menganalisa data menggunakan statistik serta meng-identifikasi korelasi atau pola yang terkandung di dalam data. Data Analytic, Data Engineering, Data Management, Data Visualization, Programming Language. Data Scientist Memproses data mulai dari pengumpulan, validasi, permodelan, hingga visualisasi. Big Data, Data Analytic, Data Engineering, Data Management, Data Visualization, Programming Language. Machine Learning Engineer Menerapkan algoritma-algoritma machine learning menjadi suatu sistem yang bisa diintegrasikan dengan sistem lain. Big Data, Data Management, Data Visualization, Machine Learning, Cloud Engineer, Programming, Computer Vision. AI Engineer Mengelola data yang besar dan menyusunnya menjadi kecerdasan buatan di dalam sebuah aplikasi. Big Data, Data Management, Data Visualization, Programming Language, Machine Learning, Robotic Framework. Database Administrator Mengimplementasikan prosedur keamanan untuk database, membersihkan serta mentansformasikan data. Data Engineeing, Database Architecture, Algorithm & Data Structure, Programming Language. This document created by Iqbal Hanif. Not for sale & distribution without permission from its creator.
8. Big Data Analytics Products/Services CRM & Marketing Computer Vision Text, Web, and Social Media Operations & Performance Analytic as a Service Customer Relationship Management (CRM) Customer 360 & Segmentation Recommendation System Optical Character Recognition (OCR) Face Recognition Video Analytics Satellite Imagery Analytics Web Analytics Social Media Analytics Natural Language Processing (NLP) / Text Analytics Robotic Automation Process (RPA) Executive/Operational Dashboard Geographic Information System (GIS) Media Rating Risk Scoring Marketing Campaign This document created by Iqbal Hanif. Not for sale & distribution without permission from its creator.
9. Big Data Analytics Projects Parent Company Subsidiaries Enterprises Government & Agencies Personal & Consumer Enterprise & Business Wholesale Functional Units MetraNet (Digital B2B) MelOn (Digitial B2C) Metraplasa (eCommerce) Finance & Insurance Oil & Gas Health & Pharmacy Infrastructure Agriculture Ministries Universities INTERNAL EXTERNAL This document created by Iqbal Hanif. Not for sale & distribution without permission from its creator.
10. Type of Use Cases Photo by Markus Spiske on Unsplash.This document created by Iqbal Hanif. Not for sale & distribution without permission from its creator.
11. Types of Use Cases Classification Regression Clustering Statistical Analysis/Method Visualization This document created by Iqbal Hanif. Not for sale & distribution without permission from its creator.
12. Data Demography Purchased Products Billing/ Transactions Infrastructure /Asset Service Quality Users Behavior Icons made by Freepik from www.flaticon.com. This document created by Iqbal Hanif. Not for sale & distribution without permission from its creator.
13. Tools Databases ETL Visualization Others Programming & Analytics This document created by Iqbal Hanif. Not for sale & distribution without permission from its creator.
14. Classification Algorithms Churn Prevention Risk Scoring Cross-sell & Up-sell Prospect Planning Age-Gender Prediction • Logistic Regression • Naïve Bayes • Random Forest • Extreme Gradient Boosting Steps • Data Pre-processing • Feature Selection & Engineering • Modeling (Train Set) • Evaluation (Test Set) Characteristics • Predict Label/Category • Mostly Binary Label • Evaluation Metrics: Accuracy, Sensitivity, ROC-AUC, F1-Score Icons made by Flat Icons from www.flaticon.com.This document created by Iqbal Hanif. Not for sale & distribution without permission from its creator.
15. Regression COVID-19 Prediction TV Rating Prediction Algorithms • ARIMA • FB Prophet • Random Forest • Extreme Gradient Boosting • SEIR Model Steps • Data Pre-processing • Feature Selection & Engineering • Modeling (Train Set) • Evaluation (Test Set) Characteristics • Predict Number / Continuous • Mostly Time Series • Evaluation Metrics: RMSE, MAE, MAPE Icons made by catkuro from www.flaticon.com.This document created by Iqbal Hanif. Not for sale & distribution without permission from its creator.
16. Clustering TV Audience Segmentation Customer Segmentation Algorithms • K-Means Clustering • K-Modes Clustering Steps • Data Pre-processing • Feature Selection & Engineering • Clustering (iterative) • Evaluation Characteristics • Generate Cluster for Non - Labelled Data • Evaluation Metrics: Elbow Method (WCSS), Silhouette Method Icons made by fjstudio from www.flaticon.com.This document created by Iqbal Hanif. Not for sale & distribution without permission from its creator.
17. Statistical Analysis/Method Sampling & Survey Campaign Evaluation Algorithms/Method • A/B Testing • Statistical Test (T-Test. ANOVA. Chi-Square) • Stratified Sampling • Clustering Sampling Characteristics • Comparing Values (statistically significant or not). • Creating suitable sampling frame for survey projects. Icons made by Freepik from www.flaticon.com.This document created by Iqbal Hanif. Not for sale & distribution without permission from its creator. Steps • Data Pre-processing • Do statistical testing or sampling based on those data
18. Visualization Reporting Online Dashboard Algorithms/Method • Descriptive Chart (Pie. Bar, Line) • Statistical Chart (Histogram, Boxplot) Characteristics • Creating Real Time Monitoring Dashboard (Executive/Operational) • Creating report or presentation for meetings Icons made by Eucalyp from www.flaticon.com.This document created by Iqbal Hanif. Not for sale & distribution without permission from its creator. Steps • Data Pre-processing • Create Visualization • Publish (for online dashboard)
19. Challenges to be Data Professionals Photo by Tim van der Kuip on Unsplash. This document created by Iqbal Hanif. Not for sale & distribution without permission from its creator.
20. Challenges Competitions Working Style Apply for The Job Working Organization Growing Up Skills This document created by Iqbal Hanif. Not for sale & distribution without permission from its creator.
21. Competitions 21% 44% 29% 6% Bachelor's Degree Master's Degree Ph.D. Others Education Level Field of Study Percentage Computer Science 3.053% Business Analytics 0.977% Physics 0.855% Information Technology 0.855% Statistics 0.733% Elctical Engineering 0.733% Applied Mathematics 0.733% Economics 0.611% Actuarial Science 0.611% Field of Study Percentage Business Analytics 4.274% Computer Science 3.175% Knowledge Engineering 2.930% Analytics 2.808% Statistics 2.564% Enterprise Business Analytics 2.564% Economics 0.733% Information Technology 0.611% Computer Engineering 0.611% Bachelor Master https://towardsdatascience.com/i-wasnt-getting-hired-as-a-data-scientist-so-i-sought-data-on-who-is-c59afd7d56f5 1. Try to be more outstanding, or Tips: Source: 2. Find jobs with specific education requirement. This document created by Iqbal Hanif. Not for sale & distribution without permission from its creator.
22. Working Style Routine Based Project Based • Stable Time of Work • Less Mobility • Consistent Type of Work • Intensive communication with peers. • Fluctuate Time of Work • More Mobility • Various Type of Work • Broad Connection 1. Find suitable job with suitable working style. Tips 2. Please be mind that not all of companies have flexible exit system. Icons made by Freepik from www.flaticon.com.This document created by Iqbal Hanif. Not for sale & distribution without permission from its creator.
23. Applying for A Job Administration / Document Selection Computer / Online Test Psychology Assessment Interview Medical Check Up 1. Prepare required documents (CV, TOEFL, Degree Cert., etc.) 2. Contact recruiters / PIC if have some questions about ducuments 3. Don’t Lie! Your document will be verified 1. Check the test schedule 2. Prepare computer and connection for the test 3. Study, but don’t forget to have enough rest 1. Check the assessment schedule and location (if offline) 2. Do benchmarking about psychology assessment 3. Take a good rest, it will be exhausting. 1. Check the interview schedule and location (if offline) 2. Guess the possible questions 3. Prepare the best introduction and closing statement (talk, gesture, etc.) 1. Check the medical check up schedule and location 2. Follow the instruction (e.g: last time to eat 3. Don’t forget to exercise and eat healthy food before the check up Icons made by Smashicons from www.flaticon.com.This document created by Iqbal Hanif. Not for sale & distribution without permission from its creator.
24. Data Scientist Data Engineer Developer Tribe UI/UX Designer Platform Engineer Organizational Structure 1. Don’t hesitate to ask about the structure. Tips 3. Be polite! 2. Then, please ask the right person if you need any help This document created by Iqbal Hanif. Not for sale & distribution without permission from its creator.
25. Growing Up Skills rank 2015 change in rank 2018 1Python [+1]Machine Learning 2Machine Learning [+2]Data Science 3R [ -2]Python 4Data Science [ -1]R 5Apache Spark [+5]SQL 6Data Mining [+3]Statistics 7Hadoop [new]Tableau 8Data Analysis [new]Data Visualization 9Statistics [new]NLP 10SQL [ -5]Apache Spark rank 2015 change in rank 2018 1Machine Learning [new]TensorFlow 2Python [ -1]Machine Learning 3Apache Spark [+1]Deep Learning 4Deep Learning [ new]Keras 5Algorithms [ -2]Apache Spark 6Java [new]NLP 7Big Data [new]Computer Vision 8Hadoop [ -6]Python 9Data Science [ -- ]Data Science 10C++ [ new]AWS Data Science Specialist Machine Learning Engineer http://www3.weforum.org/docs/WEF_Data_Science_In_the_New_Economy.pdf Source: 1. Don’t stop learning, find your own suitable learning platform Tips: 2. Try to implement your new skills in real case problem. This document created by Iqbal Hanif. Not for sale & distribution without permission from its creator.
26. Want To Join Us? Great People Trainee Program (GPTP) https://rekrutmen.telkom.co.id/ @livingintelkom Professional Hire (CODEX) Internship (DDB) https://codex.works/ @codex.telkom https://internship.ddbtelkom.id/ @internship_ddbtelkom/ Thank you, See you in Telkom Group! This document created by Iqbal Hanif. Not for sale & distribution without permission from its creator.