Business Analytics - Learning Guide

Get started with Learning Skills for Business Analytics with this Learning Path


Overview

Data Science has become a become a big buzzword in the last 5 - 6 years and for the right reasons. The amount of data generated each year is increasing exponentially. Consequently, the need to analyze the burgeoning data lakes and employ it in driving business goals is increasing as well. However, at the same time there are is a segment of detractors in the field, who claim that the cost of using data analysis is more than the return it generates. This may or may not be true depending on your understanding of data analysis, business analytics, and data science.

The data engineering, data science, and business analytics require a good common set of skills at their base. However, they are very different at the professional level. And as you begin to learn and specialize, you need to be aware of the difference and skills you need to pick up. So in this learning path, we would understand some of that difference and cover learning path for the business side of data science.

Difference between Data Science and Business Analytics

Data Science is not meant for every organization and at all stages. One of the major reasons, that executives are dissatisfied with Data Science is its over-popularity, high premium tools, less experienced but costly employees and little returns. However, this is mostly the case with organizations that jump directly in using the advanced use cases of data science even for small needs. The road to data science goes via data analysis of daily business requirements that often go missed. Data science is casually used for the entire range of from :

  1. Simple data analysis needs
  2. Intermediate and complex business use cases like A/B testing, Churn analysis, Customer Lifetime Value
  3.  Advanced machine learning based solutions, like clustering, predictive, prescriptive, sentiment analytics

It's not just the industry, even many students who have been entering into the field can have a mismatch in expectations in what they want to learn and what they want to be working on when employed. We can't escape the fact that the whole spectrum of data based skillset is referred to under the Data Science umbrella term. 

So while the industry is gradually orienting and aligning job roles with particular data based skillset. We as learners need to be sure as well as to what we want to learn. In order to simplify this for our own Quick-Learners, we have created this learning path for those who want to understand and specialize in the business side of data science or Business Analytics.

Beginners Topics to Focus in Business Analytics [Days 1 - 4]

  1. Elementary Statistics, Descriptive statistics, Statistical inferences
  2. Measurement Scale- Categorical Vs Continuous Data
  3. Data sources, Cross-Sectional, Time-Series
  4. Distributions and Data Frequency
  5. Histograms, Bar Graphs, Line Graphs, Pareto Charts, Pie Charts
  6. Histograms, Skewness, Cumulative Distributions
  7. Fishbone, Cross-Tabulation, Scatter diagrams. Summarizing data and analysis examples.

For beginners, you can check out one the below Business Analytics courses:

Beginners-II Topics to Focus in Business Analytics [Days 6 - 10]

  1. Understanding Sample and Population Means, Median, Mode
  2. Percentiles, Quartiles
  3. Variabilities- Range, Interquartile Range, Variance, Standard Deviation, Co-variance
  4. Measurement of Skewness and Z- Score, Empirical Rule, and Outliers

Intermediate Topics to Focus in Business Analytics [Days 11 - 16]

  1. Hypothesis testing, Statistical Significance
  2. Defining Hypothesis, Accepting, Reject and interpretation of Null Hypothesis
  3. T-Tests, Difference between 1-tail and 2 tail T-tests
  4. P- Value, Confidence Values, interpretation, advantage, and limitations of P-Value 
  1. Optimization and A/B Testing- Design of Experiment, KPIs, sample size
  2. Optimization and A/B Testing- Analysis, Segmentation, trend analysis
  3. Customer Life Time Value (CLV) Analysis
  4. Marketing RFM Matrix analysis- Recency- Frequency-Monetary
  5. Net Promoter Score Analysis

Complementing Skills to Learn in Business Analytics

Apart from data analysis, a big part of business analytics is to convey the business insights derived to different internal and external stakeholders. So apart from knowing how to analyze data, one should also develop skills to convey the insights and recommendation. Also most times, a business analyst may not have a very specific business problem. One would need to understand the situation as a whole, come up with assumptions, problem statements, and alternate solutions.

So here are some complementing skills that you'll need to get started in a professional business analytics job:

  1. SQL
  2. Excel
  3. Matlab, R, Python
  4. Domain or Industry specific business knowledge
  5. Logical and Complex Problem Solving
  6. Storytelling with Data, Presentation Skills

Specialized Paths in Business Analytics

  1. Business Analysis with either Python, R, SAS
  2. Marketing Analytics
  3. Pricing Analytics
  4. Financial Analytics
  5. Healthcare Analytics
  6. Supply Chain Analytics

Below are some specialized courses:

Popular Job Titles with Business Analytics Skills

  1. Analytics Consultant
  2. Data Analyst
  3. Business Analyst
  4. Marketing Data Analyst
  5. Financial Data Analyst
  6. Analytics Manager
  7. Data Scientist/ Staff Data Scientist

Conclusion

We hope that this would help you make an informed decision in starting in either business analytics or data science. Let us know in case of any further questions or feedback here, QuickCode team will be here to help you with your learning needs.