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<article type="text" id="editor" contenteditable="true" onkeydown="saveopenfile()" class="" fileopenid="126444733602">INSTRUCTIONS FOR PRESENTER
THERE ARE A FEW PLACES WHERE "[INSERT]" IS USED AS A PLACEHOLDER FOR THE CONVENOR TO ADD THEIR INSIGHT, EXAMPLES, AND/OR COMMENTARY IF HELPFUL TO BOLSTER THE SCRIPT.

INTRODUCTION 
WHO ARE YOU, WHAT ARE YOU GOING TO BE TALKING ABOUT, AND WHY?
HI! MY NAME IS DR MILT MAVRAKAKIS AND I AM A GUEST LECTURER IN THE STATISTICS DEPARTMENT AT THE LONDON SCHOOL OF ECONOMICS AND POLITICAL SCIENCE, WHERE I TEACH VARIOUS UNDERGRADUATE AND POSTGRADUATE COURSES. I AM PARTICULARLY INTERESTED IN STATISTICAL COMMUNICATION AND INTERACTIVE TECHNIQUES IN THE CLASSROOM.
WORD COUNT: ~ 347
DURATION: ~ 2:30 MINUTES



MY STATISTICS TEACHING IS INTERTWINED WITH MY PROFESSIONAL CAREER AS A DATA SCIENTIST; I AM A SENIOR QUANTITATIVE ANALYST AND HEAD OF QUANT TRAINING AT SMARTODDS, A SPORTS BETTING CONSULTANCY. THE MAIN FOCUS OF MY JOB IS THE STATISTICAL MODELLING OF SPORTS AND BETTING MARKETS. 

YOU ARE CURRENTLY ENROLLED IN COURSE 3 OF THE CAREER ACCELERATOR TO PURSUE A CAREER AS A DATA ANALYST. SOME OF YOU MIGHT FEEL THIS JOURNEY HAS BEEN CHALLENGING, EXCITING, REWARDING, CONFUSING, OR MOST PROBABLY A MIXTURE OF THESE. THIS IS ABSOLUTELY NORMAL, AND THESE FEELINGS SHOULD NOT DETER YOU FROM CONTINUING ON THIS PATH. 

AS YOU ARE NOW IN THE FINAL STRETCH OF THE CAREER ACCELERATOR, YOU MIGHT START TO FEEL ANXIOUS ABOUT THE NEXT STEPS. QUESTIONS MIGHT POP UP, LIKE:
* AM I PREPARED? 
* WILL I BE ABLE TO LAND AN INTERVIEW?
* WHAT TYPE OF QUESTIONS WILL THE INTERVIEWER ASK ME?
* HOW CONFIDENT AM I WITH MY NEW SKILLS?

THE CAREER ACCELERATOR WAS PUT TOGETHER WITH THE ASSISTANCE OF INDUSTRY EXPERTS, HEADHUNTERS, DATA ANALYSTS, AND ACADEMICS. WE WORKED AS A TEAM TO PREPARE YOU AS BEST WE COULD FOR YOUR FUTURE CAREER. 

THE TOP LISTED TECHNICAL AND SOFT SKILLS REQUIRED TO BECOME A DATA ANALYST ARE TECHNIQUES SUCH AS DATA CLEANING, DATA VISUALISATION, AND DATA WRANGLING; FAMILIARITY WITH TOOLS SUCH AS EXCEL, SQL, TABLEAU, PYTHON, R; AND OF COURSE, CRITICAL THINKING AND COMMUNICATION. ALL OF THESE HAVE BEEN COVERED TO DATE.

IN THIS MASTERCLASS, IM GOING TO ANSWER A FEW QUESTIONS ASKED FREQUENTLY IN INTERVIEWS ABOUT THE CONTENT OF COURSE 3. FOR EXAMPLE:

* WHAT IS THE DIFFERENCE BETWEEN DATA MINING AND DATA PROFILING? WHERE DOES DATA WRANGLING FIT IN?

* AS A DATA ANALYST, WHY DO I HAVE TO KNOW ABOUT MACHINE LEARNING ALGORITHMS? 

* WHAT ARE THE COMMON DATA ANALYSIS MISTAKES TO AVOID?


AS WE WORK THROUGH THESE QUESTIONS, YOU WILL START TO SEE HOW THE THREE COURSES FIT TOGETHER TO PREPARE YOU FOR THAT MUCH-ANTICIPATED DATA ANALYST CAREER.

LETS GET STARTED!


QUESTION 1 
WHAT IS THE DIFFERENCE BETWEEN DATA MINING AND DATA PROFILING? WHERE DOES DATA WRANGLING FIT IN?
WORD COUNT: 470<span style="color: tomato;">m</span><p></p>
DURATION: ~ 3:20 MINUTES
IN THE PREVIOUS TWO COURSES OF THE CAREER ACCELERATOR, YOU PERFORMED DATA WRANGLING IN EXCEL, TABLEAU, SQL, PYTHON AND R. ALTHOUGH THE FINAL OBJECTIVE IS THE SAME, THE PROGRAMMING MIGHT DIFFER. TO REITERATE, DATA WRANGLING IS WHEN YOU TAKE RAW DATA  OFTEN FROM MULTIPLE SOURCES AND IN DIFFERENT FORMATS  AND TRANSFORM IT INTO A CLEAN, STRUCTURED, AND ORGANISED FORM FOR DATA ANALYSIS. IN PROGRAMMING LANGUAGES SUCH AS PYTHON AND R, WE USE FUNCTIONS TO GROUP, SORT, CONCATENATE, JOIN, AND VALIDATE DATA. 

AS A DATA ANALYST, YOU MIGHT OFTEN COME ACROSS TERMINOLOGIES SUCH AS DATA MINING AND DATA PROFILING. UNDERSTANDING THESE TERMS MIGHT HELP YOU IN AN INTERVIEW OR DISCUSSION WITH STAKEHOLDERS. 

IN THE FIRST COURSE, DATA ANALYTICS FOR BUSINESS, YOU LEARNED THE IMPORTANCE OF DATA QUALITY. DATA PROFILING IS THE PROCESS OF EXAMINING AND ANALYSING THE DATA TO CREATE USEFUL SUMMARIES, WHICH CAN HELP US ASSESS ITS QUALITY AND DETERMINE IF IT IS SUITABLE FOR OUR APPLICATION. FOR EXAMPLE, REAL-TIME COVID DATA NEEDS TO BE OF HIGH QUALITY AS GOVERNMENTS ACROSS BORDERS RELY ON IT TO MAKE DECISIONS WITH LIFE-CHANGING CONSEQUENCES. 

YOU HAVE SEEN THAT EXCEL HAS PROFILING TOOLS WHICH CAN HELP US CLEAN, TRANSFORM AND QUERY THE DATA SET. IN SQL WE OFTEN LOOK AT AGGREGATE STATISTICS SUCH AS THE NUMBER OF CUSTOMERS OR MISSING VALUES. PYTHON, OR MORE SPECIFICALLY PANDAS, HELPS YOU EXPLORE THE STRUCTURE OF <span style="color: tomato;">m</span><p></p>THE DATA SET WITH VARIOUS FUNCTIONS, WHILE R DOES THE SAME WITH THE TIDYVERSE PACKAGES, AMONG OTHER WAYS. 

DATA MINING IS A PROCESS THAT FOLLOWS RIGHT AFTER YOU HAVE PUT THE DATA IN A USABLE FORM. THE AIM IS TO TURN RAW STRUCTURED DATA STORED IN RELATIONAL DATABASES OR SPREADSHEETS INTO USEFUL INFORMATION BY IDENTIFYING PATTERNS. FOR EXAMPLE, BY EXPLORING HISTORICAL DATA, ORGANISATIONS CAN LEARN MORE ABOUT THEIR CUSTOMERS OR THEIR EMPLOYEES TO DEVELOP EFFECTIVE MARKETING AND WORKPLACE STRATEGIES.

THERE ARE VARIOUS TYPES OF DATA MINING. FOR EXAMPLE, TEXT MINING IS WHEN THE RAW DATA CONSISTS OF TEXT. THIS COULD BE  IN THE FORM OF EMAILS, CUSTOMER FEEDBACK, CALL CENTRE TRANSCRIPTS, OR PUBLICALLY ACCESSIBLE CONTENT SUCH AS WEBSITES, BOOKS, OR ARTICLES.

IN SUMMARY, DATA PROFILING IS ABOUT EXAMINING THE DATA AND PRODUCING SUMMARIES OR STATISTICS, WHEREAS DATA MINING IS ABOUT IDENTIFYING PATTERNS IN THE DATA, WHICH CAN LEAD TO INSIGHTS.

[PLEASE INSERT MORE POINTS OF DIFFERENCES, IF HELPFUL]


QUESTION 2
 AS A DATA ANALYST, WHY DO I HAVE TO KNOW ABOUT MACHINE LEARNING ALGORITHMS? 
WORD COUNT: 437
DURATION: ~ 3:00 MINUTES

AS A DATA ANALYST YOU CERTAINLY WON'T NEED TO KNOW EVERYTHING ABOUT MACHINE LEARNING AND THE VARIOUS MATHEMATICAL MODELS INVOLVED. YOU WILL NOT BE EXPECTED TO COME UP WITH A NOVEL ALGORITHM ALL BY YOURSELF. HOWEVER, GIVEN THAT YOU ARE LOOKING TO LAND A JOB IN THE DATA INDUSTRY, IT IS ESSENTIAL TO HAVE A BASIC UNDERSTANDING OF THE MAIN ML ALGORITHMS. AS YOU WILL LEARN THROUGHOUT THIS CAREER ACCELERATOR, THERE ARE PYTHON AND R LIBRARIES AVAILABLE FOR YOU TO IMPLEMENT THESE ALGORITHMS -- AND THEY ARE MOSTLY STRAIGHTFORWARD TO USE, THOROUGHLY TESTED, AND RELIABLE -- BUT IT IS VERY IMPORTANT THAT YOU HAVE A HIGH-LEVEL UNDERSTANDING OF HOW THE ALGORITHMS WORK. THIS WILL HELP YOU CHOOSE THE RIGHT TOOLS FOR EACH PROBLEM, AND FIGURE OUT WHAT TO DO WHEN THINGS GO WRONG. 

A DATA ANALYST TYPICALLY FOCUSES ON PROCESSING RAW DATA TO CREATE MEANINGFUL INSIGHTS. THEY WORK TO IDENTIFY PATTERNS AND COMMUNICATE THE RESULTING INSIGHTS IN A WAY THAT IS EASY TO UNDERSTAND AND LEADS TO BUSINESS ACTIONS. MANY OF THE ALGORITHMS INVOLVED IN THIS ANALYSIS, WHICH WE USE FOR TASKS SUCH AS CLASSIFICATION AND CLUSTERING, COME FROM MACHINE LEARNING. FOR EXAMPLE, TO ESTIMATE THE PRICE OF YOUR PROPERTY OR OTHER SIMILAR PROPERTIES IN YOUR NEIGHBOURHOOD WOULD NEED SOME CAREFUL APPLICATION OF A MACHINE LEARNING ALGORITHM.

LETS LOOK AT A COUPLE MORE EXAMPLES.
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WALMART, THE MULTINATIONAL RETAIL CORPORATION, GENERATES DATA  LITERALLY BY THE SECOND  WITH ONLINE AND IN-STORE SHOPPING. THE DATA IS ANALYSED TO PREDICT CUSTOMER SATISFACTION AND SHOPPING TRENDS. RECENTLY, WALMART INCORPORATED MACHINE LEARNING TO OPTIMISE DELIVERY ROUTES, AND FACIAL RECOGNITION TO TRACK IN-STORE CUSTOMER SATISFACTION. FOR EXAMPLE, CLUSTERING AND CLASSIFICATION PREDICTIVE MODELS WERE USED TO CREATE SHORTER DELIVERY ROUTES, THUS SPEEDING UP DELIVERIES AND SAVING COURIER COSTS. 

CLICKATELL, A GLOBAL LEADER IN CHAT COMMERCE, INCORPORATED MACHINE LEARNING AS PART OF THEIR STANDARD DATA ANALYTICS REGIME. THEY REALISED THAT CUSTOMERS REACT POSITIVELY TO A FASTER TURNAROUND ON RESPONSES AND HOW THEY HANDLE CUSTOMER QUERIES. WITH MACHINE LEARNING, THEY CAN ALMOST IMMEDIATELY RESPOND TO CUSTOMER NEEDS, GIVING THEM A COMPETITIVE EDGE. FOR EXAMPLE, CREATING CHATBOTS TO ANSWER FREQUENTLY ASKED QUESTIONS BASED ON HISTORICAL DATA. IF THE CHATBOT CANNOT ANSWER THE QUESTION, THE CUSTOMER IS REFERRED TO AN OPERATOR.<span style="color: tomato;">m</span><p></p>


[PLEASE INSERT MORE EXAMPLES, IF HELPFUL]


   DS THE STRATEGY OF THE BUSINESS, HOW DATA IS COLLECTED, WHY AND HOW THE DATA INFORMS THE BUSINESS. THIS WILL ENSURE THAT YOU CAN SPOT HIDDEN PROBLEMS WITHIN A DATA SET EARLY ON AND PLAN A COURSE OF ACTION TO MITIGATE ANY RISKS. WITH THAT IN MIND, DON'T FORGET TO LOOK AT BASIC DESCRIPTIVE STATISTICS. NUMERICAL SUMMARIES (SUCH AS THE MEAN OR STANDARD DEVIATION) AND SIMPLE PLOTS WILL OFTEN REVEAL ISSUES WITH THE DATA. 

SECOND, TRY TO BE AS METHODICAL AS POSSIBLE WITH YOUR CODE. ORGANISE INTO SCRIPTS AND KEEP A SCRAPBOOK -- A NOTEBOOK WITH CODE SNIPPETS, FORMULAE, AND NOTES ON PROCEDURES THAT WORKED ON PREVIOUS PROJECTS, OR EVEN USEFUL RESULTS YOU FIND ONLINE. ONE OF THE HUGE ADVANTAGES OF LANGUAGES WITH A VERY LARGE USER BASE (SUCH AS PYTHON OR R) IS THAT THERE ARE ALWAYS PEOPLE OUT THERE TRYING TO SOLVE PROBLEMS SIMILAR TO YOURS. DONT BE AFRAID TO SEEK THEIR HE…</article>