Discovering who we are is often seen as a complex process. It can be when it comes to matters of the psyche. But people
Be familiar with linear regression and other advanced regression methods. Be competent in using packages such as scikit-learn and caret for linear regression model building. Have the following competencies:
We remark here that these are approximate values only. The amount of time required to gain a certain level of competence depends on your background and how much amount of time you are willing to invest in your data science studies. Typically, individuals with a background in an analytic discipline such as physics, mathematics, science, engineering, accounting, or computer science would require less time compared to individuals with backgrounds not complementary to data science.
Geometric Component: Here is where you decide what kind of visualization is suitable for your data, e.g., scatter plot, line graphs, bar plots, histograms, Q-Q plots, smooth densities, boxplots, pair plots, heatmaps, etc.
Be able to understand the essential components of good data visualization. Be able to use data visualization tools including Python’s matplotlib and seaborn packages; and R’s ggplot2 package. Should understand the essential components of good data visualization:
Level 1 competency can be achieved within 6 to 12 months. Level 2 competencies can be achieved within 7 to 18 months. Level 3 competencies can be achieved within 18 to 48 months.
In summary, we’ve discussed the 3 levels of data science. Level 1 competency can be achieved within 6 to 12 months. Level 2 competencies can be achieved within 7 to 18 months. Level 3 competencies can be achieved within 18 to 48 months. It all depends on the amount of effort invested and the background of each individual.
Understand several metrics for accessing the quality of a classification algorithm such as accuracy, precision, sensitivity, specificity, recall, f-l score, confusion matrix, ROC curve.
Ethical Component: Here, you want to make sure your visualization tells the true story. You need to be aware of your actions when cleaning, summarizing, manipulating, and producing a data visualization and ensure you aren’t using your visualization to mislead or manipulate your audience.
Often they’ll have a specific problem. But, beyond that, they’ll want to dig down into how they roll in the world: their thoughts, moods, emotions, behaviours, fears, hopes and dreams.
Data Component: An important first step in deciding how to visualize data is to know what type of data it is, e.g., categorical data, discrete data, continuous data, time-series data, etc.
Getting to know yourself is a work in progress — for life. No-one ever gets to shut their personal file and say: “that’s it, job done, I know everything there is to know.”
In summary, we’ve discussed the 3 levels of data science. Level 1 competency can be achieved within 6 to 12 months. Level 2 competencies can be achieved within 7 to 18 months. Level 3 competencies can be achieved within 18 to 48 months. It all depends on the amount of effort invested and the background of each individual.
At level one, a data science aspirant should be able to work with datasets generally presented in comma-separated values (CSV) file format. They should have competency in data basics; data visualization; and linear regression.
Mapping Component: Here, you need to decide what variable to use as your x-variable and what to use as your y-variable. This is important especially when your dataset is multi-dimensional with several features.
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