Key Skills for Data Analysts
The Top skills for Data Analysts as explained by Northeastern
https://www.northeastern.edu/graduate...
Structured Query Language (SQL)
Microsoft Excel
Critical Thinking
R or Python-Statistical Programming
Data Visualization
Presentation Skills
Machine Learning
The DIFME CONSORTIUM
http://difme.eu/
was set up to enhance entrepreneurs' capacity to undertake responsible financial choices and to understand digital techniques readily available to boost their enterprise. Here we sign post skills that are important for developing business intelligence consistent with module 9 of the DIFME project and these largely align to northeastern's road map as set out above. We also endeavour to put each skill set in context and provide some limited tuition that makes these skills accessible to a relatively broad constituency of start-ups, entrepreneurs and learners. We describe below the process and learning outcomes.
SQL, dply libraries from R and pandas libraries from python provide important data manipulation tools.
R and Rstudio is a powerful data analytic tool available to entrepreneurs for free:
• RStudio Installation Process and Test
• RStudio Cloud set up and Installation
Data Transformation includes the basic tasks of finding, cleaning and processing data using Google Colab can be set up for free to harness R tidyverse and pandas libraries.
• Set up and Install Google Colab
• Two ways to run R code in Google Colab
ggplot2 from R
https://sites.google.com/view/vinegar...
and matplotlib and seaborn (to name a few) libraries from python enable Visualization combined with Transformation tools to explore data in a systematic way.
https://sites.google.com/view/vinegar...
Engage in exploratory data analysis, (or EDA) and to parse through Big Data. These tools from R and Python leverage the graphing capability for free in ggplot2, matplotlib, seaborn etc. The ggplot2 syntax executable in R (and incidentally Python) provides a simplified grammar for producing “elegant graphics for data analysis”.
Data Analytics Tools include using Excel
Developing a broad insight and understanding of data analytics tools and the ability to extract useful knowledge from data may start with Excel. As your data grows you become more reliant on dplyr and pandas. These provide the groundwork to develop a mastery of basic statistical techniques, employ basic OLS and random forest modeling for making forecasts. R and Python can be important for reporting, presenting and critically evaluating business/scientific intelligence from a range of techniques spanning simple regression to Machine Learning techniques.
Develop Excel Macro and VBA skills. We demonstrate how to implement OLS modeling in Excel. We demonstrate how to estimate value of Employee Stock Options - a non trivial exercise for startups.
https://sites.google.com/view/vinegar...
https://sites.google.com/view/vinegar...
Also we provide some training from scratch on how to automate the estimation of mortgage repayments using VBA.
Develop EDA skills in R and Python environments - RStudio/Python Anaconda / Google Colab
Perform Data Analysis in R and Python. Develop Exploratory Data Analysis and pre-modelling using R tidyverse
https://sites.google.com/view/vinegar...
and Python Pandas libraries:
https://sites.google.com/view/vinegar...
We develop a series of tutorials to explain some of the powerful data transformation and manipulation features of Pandas. These are excellent for preparing professional style reports.
These cutting edge packages can be transformative in promoting collaboration and disseminating ideas through sharing business intelligence. In particular, the Tidyverse umbrella package from R can be used to tease out many key areas of data analytics. Tidyverse R can also be installed in Google Colab.
We introduce statistical modelling very gently here by making use of Excel, R and Python.
https://sites.google.com/view/vinegar...