Solved – Skills & coursework needed to be a data analyst

academiacareers

I am graduating with a bachelor's degree in applied math and I will pursue a master's degree in statistics this fall.

There are many specialized fields in applied statistics. I realize I may be more interested in becoming a data analyst, working with big data in market research or IT, but I'm not sure what kinds of skills are needed and what are the core courses I should take to become a data analyst.

I've learned some MatLab, R, SPSS, C, ruby, can't say I'm an expert on these. I'm learning SAS, because SAS seems to be what every stats master should know. (Is this really true?)

I'm a little confused right now and need some specific advice about what skills I should intensively learn and what courses I should take.

TIA.

Best Answer

Aside from the technical skills, like R or SAS, SQL will be important, and a few other higher level skills, including:

Data Manipulation: To be able to analyse data, you will frequently have to spend some considerable time acquiring the data and manipulating it into a form which can be analysed. Many statisticians will tell you that most of their time on a given project will be spent manipulating the data - so it is important to be good at this!

Understanding: Many people vastly under estimate the amount of time that is required just to understand a complex dataset. In bygone days one had to serve time apprenticed to a master crafts man, with a dataset you have to spend time looking at the various facets of the data and understanding it's dimensions and missing data and talking to people to try to understand the data. Again you will spend considerable time doing this, it requires practice to build up this skill!

Visualization: Going hand in hand with the above is visualization. Knowing how to plot data to help gain understanding is important. Later when you want to show somebody else what you have found in the dataset, a carefully created picture says a thousand words.

Requirements: One of the hardest to learn is requirements gathering. Your customer will frequently not be clear what they want in their own head, and even less clear in what they say to you.

Related Solutions

Solved – What data and statistics skills are currently in high demand and where are they in high demand

A friend of mine has suggested that the software industry primarily needs "big data" skills, not statistics skills per se.

While partially agreeing with your friend's comment, I would like to point out that in any industry, Big data tools are opted, only if all the V's are satisfied.

I work as the head of data science at a leading customer support company. Here, I do data hacking both for the product and also for the growth of the company.

I primarily use time series analysis techniques for churn prediction and sales analysis. This also includes the behavioural analysis of the customers, competition and the industry.

On the product side, we use a range of techniques starting from sentiment analysis using LSTM's, recommendation algorithms, etc.

But the core focus lies on time series analysis. The general workflow would be:

Cleaning and moulding the data.
the exploratory and explanatory analyses which involves identification of seasonality, trends and cycles. So, one need to explore correlations, auto-correlations, and several univariate and bivariate statistics; along with extensive plotting including the scatter, AFC, PAFC curves.
Now comes the forecasting part, where various models are tested each other, taking the step - 2 into serious consideration.

Tools used by me: R, Python and Excel sometimes.

And even the blend of data science and growth hacking have proven to do magic in the domain of marketing. So, the demand for statisticians and math nerds would remain as is; and is not going to decline anywhere in the near future; especially when customer focused startups are blossoming across the world.

Solved – What statistical tools does a analyst (financial) require

Maybe you want to take a look in Statistics and Finance: An Introduction by David Ruppert. It covers a wide range of topics but none in detail. The author actually written this book with engineers in mind and it's very application-oriented.

Table of Content:

1 Introduction
2 Probability and Statistical Models
3 Returns
4 Time Series Models
5 Portfolio Theory
6 Regression
7 The Capital Asset Pricing Model
8 Options Pricing
9 Fixed Income Securities
10 Resampling
11 Value-At-Risk
12 GARCH Models
13 Nonparametric Regression and Splines
14 Behavioral Finance

Best Answer

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Solved – What data and statistics skills are currently in high demand and where are they in high demand

Solved – What statistical tools does a analyst (financial) require

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