LSE Data Analytics Career Accelerator
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This repository contains all the practical activities in Python and R across three courses of the LSE Data Analytics Career Accelerator.
The online Data Analytics Career Accelerator from The London School of Economics and Political Science (LSE) aims to equip working professionals and career starters with the knowledge they need to lead critical, data-backed decision making in organisations across industries.
Over 6 months, I developed fundamental knowledge, skills and applied project experience in data analytics across enterprise databases and tools. I built coding skills in the high-demand data programming languages Python and R, and practiced their application to data projects in authentic business scenarios. I also further developed and strengthened my communication skills including data visualisation, to ensure analysis and insight to support actionable business decisions.
The content of the program includes three courses and an employer project, where I built a portfolio of evidence to showcase newly learned skills and competencies, with a strong focus on becoming a reflective practitioner, and becoming equipped with the mindset and tools to solve problems and effectively acquire new technical, business and human skills.
Identify, source and perform basic cleansing on data from various, relevant sources to support required analysis processes Conduct exploratory and descriptive analytics Establish and utilise databases to support data management and analysis Effectively communicate justified, relevant and useful insights to critical business stakeholders Identify appropriate opportunities for business value through data analytics processes Tools / languages: Tablueau, Excel, SQL Postgres, SQL Databases Assessment: Referring to a given dataset and corresponding business scenario, use SQL and Excel to identify insights through data analysis. Create a dashboard using Tableau to communicate insights alongside critical business metric sto help key stakeholders make data-informed decisions.
Utilise Python to collect and import large amounts of complex data through various approaches, including web scraping techniques Utilise Python to wrangle data for effective analysis Complete advanced analytical processes to determine critical business insights from data sets Prepare comprehensive and complex visualisations to gather insights, study trends and present insights to support critical business decisions Justify approaches taken, interpretation of insights, and recommendations Tools/languages: Python, Git/GitHub/BASH, SQL Databases Assessment: Referring to a given dataset and corresponding business scenario, use Python to perform an exploratory data analysis to uncover insights and identify potential causes. Through analysis and visualisation, determine contributing factors to trends and insights, and communicate key findings.
Apply predictive models to transform insights into actionable strategies to support business objectives Establish methodologies and develop a culture conducive to effective and ethical data-driven business practice Prepare advanced data visualisations and data stories to communicate compelling, guided narratives to effectively support business decision-making Solve business problems and justify strategic recommendations that leverage best practice and advanced data-analytic approaches Tools/languages: Python, R, Git/GitHub/BASH Assessment: Referring to a given dataset and corresponding business scenario, use Python or R to perform an exploratory data analysis to predict future outcomes. Make business recommendations based on those predictions using visualisations to uncover and communicate key insights.
Collaborating with fellow learners on a real-world employer project in a culmination of the skills acquired in the first three courses. The project is designed by a leading technology company to reflect the practical skills required by industry. Requires a synthesis of the methods and techniques developed, and is based on a genuine employer need and interest.
