Entry level jobs in data analytics are a great way to start a career in the field. They allow you to gain experience while also learning the ropes of your industry. They can also be a good way to decide if data analytics is right for you, and if it isn’t, they’ll help you find another path in the field.
Here are some examples of entry level jobs in data analytics:
-Data Entry Clerk
-Data Mining Analyst
-Business Intelligence Analyst
Entry Level Jobs In Data Analytics
Data analysis is a process of inspecting, cleansing, transforming, and modelling data with the goal of discovering useful information, informing conclusions, and supporting decision-making.[1] Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, and is used in different business, science, and social science domains.[2] In today’s business world, data analysis plays a role in making decisions more scientific and helping businesses operate more effectively.[3]
Data mining is a particular data analysis technique that focuses on statistical modelling and knowledge discovery for predictive rather than purely descriptive purposes, while business intelligence covers data analysis that relies heavily on aggregation, focusing mainly on business information.[4] In statistical applications, data analysis can be divided into descriptive statistics, exploratory data analysis (EDA), and confirmatory data analysis (CDA).[5] EDA focuses on discovering new features in the data while CDA focuses on confirming or falsifying existing hypotheses.[6][7] Predictive analytics focuses on the application of statistical models for predictive forecasting or classification, while text analytics applies statistical, linguistic, and structural techniques to extract and classify information from textual sources, a species of unstructured data. All of the above are varieties of data analysis.[8]
Data integration is a precursor to data analysis, and data analysis is closely linked to data visualization and data dissemination.[9]
Contents
- 1The process of data analysis
- 2Quantitative messages
- 3Techniques for analyzing quantitative data
- 4Analytical activities of data users
- 5Barriers to effective analysis
- 6Other topics
- 7Practitioner notes
- 8Free software for data analysis
- 9International data analysis contests
- 10See also
- 11References
- 12Further reading
The process of data analysis[edit]
Data science process flowchart from Doing Data Science, by Schutt & O’Neil (2013)
Analysis, refers to dividing a whole into its separate components for individual examination.[10] Data analysis, is a process for obtaining raw data, and subsequently converting it into information useful for decision-making by users.[1] Data, is collected and analyzed to answer questions, test hypotheses, or disprove theories.[11]
There are several phases that can be distinguished, described below. The phases are iterative, in that feedback from later phases may result in additional work in earlier phases.[13] The CRISP framework, used in data mining, has similar steps.
Data requirements
The data is necessary as inputs to the analysis, which is specified based upon the requirements of those directing the analysis (or customers, who will use the finished product of the analysis).[14][15] The general type of entity upon which the data will be collected is referred to as an experimental unit (e.g., a person or population of people). Specific variables regarding a population (e.g., age and income) may be specified and obtained. Data may be numerical or categorical (i.e., a text label for numbers).[13]
Data collection[edit]
Data is collected from a variety of sources.[16][17] The requirements may be communicated by analysts to custodians of the data; such as, Information Technology personnel within an organization.[18] The data may also be collected from sensors in the environment, including traffic cameras, satellites, recording devices, etc. It may also be obtained through interviews, downloads from online sources, or reading documentation.[13]
Data processing
The phases of the intelligence cycle used to convert raw information into actionable intelligence or knowledge are conceptually similar to the phases in data analysis.
Data, when initially obtained, must be processed or organized for analysis.[19][20] For instance, these may involve placing data into rows and columns in a table format (known as structured data) for further analysis, often through the use of spreadsheet or statistical software.[13]
Data cleaning
Main article: Data cleansing
Once processed and organized, the data may be incomplete, contain duplicates, or contain errors.[21][22] The need for data cleaning will arise from problems in the way that the datum are entered and stored.[21] Data cleaning is the process of preventing and correcting these errors. Common tasks include record matching, identifying inaccuracy of data, overall quality of existing data, deduplication, and column segmentation.[23] Such data problems can also be identified through a variety of analytical techniques. For example; with financial information, the totals for particular variables may be compared against separately published numbers that are believed to be reliable.[24][25] Unusual amounts, above or below predetermined thresholds, may also be reviewed. There are several types of data cleaning, that are dependent upon the type of data in the set; this could be phone numbers, email addresses, employers, or other values.[26][27] Quantitative data methods for outlier detection, can be used to get rid of data that appears to have a higher likelihood of being input incorrectly.[28] Textual data spell checkers can be used to lessen the amount of mis-typed words. However, it is harder to tell if the words themselves are correct.[29]
Exploratory data analysis
Once the datasets are cleaned, they can then be analyzed. Analysts may apply a variety of techniques, referred to as exploratory data analysis, to begin understanding the messages contained within the obtained data.[30] The process of data exploration may result in additional data cleaning or additional requests for data; thus, the initialization of the iterative phases mentioned in the lead paragraph of this section.[31] Descriptive statistics, such as, the average or median, can be generated to aid in understanding the data.[32][33] Data visualization is also a technique used, in which the analyst is able to examine the data in a graphical format in order to obtain additional insights, regarding the messages within the data.[13]
Modelling and algorithms
Mathematical formulas or models (known as algorithms), may be applied to the data in order to identify relationships among the variables; for example, using correlation or causation.[34][35] In general terms, models may be developed to evaluate a specific variable based on other variable(s) contained within the dataset, with some residual error depending on the implemented model’s accuracy (e.g., Data = Model + Error).[36][11]
Inferential statistics, includes utilizing techniques that measure the relationships between particular variables.[37] For example, regression analysis may be used to model whether a change in advertising (independent variable X), provides an explanation for the variation in sales (dependent variable Y).[38] In mathematical terms, Y (sales) is a function of X (advertising).[39] It may be described as (Y = aX + b + error), where the model is designed such that (a) and (b) minimize the error when the model predicts Y for a given range of values of X.[40] Analysts may also attempt to build models that are descriptive of the data, in an aim to simplify analysis and communicate results.[11]
Data product
A data product is a computer application that takes data inputs and generates outputs, feeding them back into the environment.[41] It may be based on a model or algorithm. For instance, an application that analyzes data about customer purchase history, and uses the results to recommend other purchases the customer might enjoy.[42][13]
Communication
Data visualization is used to help understand the results after data is analyzed.[43]
Main article: Data visualization
Once data is analyzed, it may be reported in many formats to the users of the analysis to support their requirements.[44] The users may have feedback, which results in additional analysis. As such, much of the analytical cycle is iterative.[13]
When determining how to communicate the results, the analyst may consider implementing a variety of data visualization techniques to help communicate the message more clearly and efficiently to the audience.[45] Data visualization uses information displays (graphics such as, tables and charts) to help communicate key messages contained in the data.[46] Tables are a valuable tool by enabling the ability of a user to query and focus on specific numbers; while charts (e.g., bar charts or line charts), may help explain the quantitative messages contained in the data.[47]
Quantitative messages
Main article: Data visualization
A time series illustrated with a line chart demonstrating trends in U.S. federal spending and revenue over time.
A scatterplot illustrating the correlation between two variables (inflation and unemployment) measured at points in time.
Stephen Few described eight types of quantitative messages that users may attempt to understand or communicate from a set of data and the associated graphs used to help communicate the message.[48] Customers specifying requirements and analysts performing the data analysis may consider these messages during the course of the process.[49]
- Time-series: A single variable is captured over a period of time, such as the unemployment rate over a 10-year period. A line chart may be used to demonstrate the trend.[50]
- Ranking: Categorical subdivisions are ranked in ascending or descending order, such as a ranking of sales performance (the measure) by salespersons (the category, with each salesperson a categorical subdivision) during a single period.[51] A bar chart may be used to show the comparison across the salespersons.[52]
- Part-to-whole: Categorical subdivisions are measured as a ratio to the whole (i.e., a percentage out of 100%). A pie chart or bar chart can show the comparison of ratios, such as the market share represented by competitors in a market.[53]
- Deviation: Categorical subdivisions are compared against a reference, such as a comparison of actual vs. budget expenses for several departments of a business for a given time period. A bar chart can show the comparison of the actual versus the reference amount.[54]
- Frequency distribution: Shows the number of observations of a particular variable for a given interval, such as the number of years in which the stock market return is between intervals such as 0–10%, 11–20%, etc. A histogram, a type of bar chart, may be used for this analysis.[55]
- Correlation: Comparison between observations represented by two variables (X,Y) to determine if they tend to move in the same or opposite directions. For example, plotting unemployment (X) and inflation (Y) for a sample of months. A scatter plot is typically used for this message.[56]
- Nominal comparison: Comparing categorical subdivisions in no particular order, such as the sales volume by product code. A bar chart may be used for this comparison.[57]
- Geographic or geospatial: Comparison of a variable across a map or layout, such as the unemployment rate by state or the number of persons on the various floors of a building. A cartogram is a typical graphic used.[58][59]
Techniques for analyzing quantitative data
See also: Problem solving
Author Jonathan Koomey has recommended a series of best practices for understanding quantitative data.[60] These include:
- Check raw data for anomalies prior to performing an analysis;
- Re-perform important calculations, such as verifying columns of data that are formula driven;
- Confirm main totals are the sum of subtotals;
- Check relationships between numbers that should be related in a predictable way, such as ratios over time;
- Normalize numbers to make comparisons easier, such as analyzing amounts per person or relative to GDP or as an index value relative to a base year;
- Break problems into component parts by analyzing factors that led to the results, such as DuPont analysis of return on equity.[25]
For the variables under examination, analysts typically obtain descriptive statistics for them, such as the mean (average), median, and standard deviation.[61] They may also analyze the distribution of the key variables to see how the individual values cluster around the mean.[62]
An illustration of the MECE principle used for data analysis.
The consultants at McKinsey and Company named a technique for breaking a quantitative problem down into its component parts called the MECE principle.[63] Each layer can be broken down into its components; each of the sub-components must be mutually exclusive of each other and collectively add up to the layer above them.[64] The relationship is referred to as “Mutually Exclusive and Collectively Exhaustive” or MECE. For example, profit by definition can be broken down into total revenue and total cost.[65] In turn, total revenue can be analyzed by its components, such as the revenue of divisions A, B, and C (which are mutually exclusive of each other) and should add to the total revenue (collectively exhaustive).[66]
Analysts may use robust statistical measurements to solve certain analytical problems.[67] Hypothesis testing is used when a particular hypothesis about the true state of affairs is made by the analyst and data is gathered to determine whether that state of affairs is true or false.[68][69] For example, the hypothesis might be that “Unemployment has no effect on inflation”, which relates to an economics concept called the Phillips Curve.[70] Hypothesis testing involves considering the likelihood of Type I and type II errors, which relate to whether the data supports accepting or rejecting the hypothesis.[71][72]
Regression analysis may be used when the analyst is trying to determine the extent to which independent variable X affects dependent variable Y (e.g., “To what extent do changes in the unemployment rate (X) affect the inflation rate (Y)?”).[73] This is an attempt to model or fit an equation line or curve to the data, such that Y is a function of X.[74][75]
Necessary condition analysis (NCA) may be used when the analyst is trying to determine the extent to which independent variable X allows variable Y (e.g., “To what extent is a certain unemployment rate (X) necessary for a certain inflation rate (Y)?”).[73] Whereas (multiple) regression analysis uses additive logic where each X-variable can produce the outcome and the X’s can compensate for each other (they are sufficient but not necessary),[76] necessary condition analysis (NCA) uses necessity logic, where one or more X-variables allow the outcome to exist, but may not produce it (they are necessary but not sufficient). Each single necessary condition must be present and compensation is not possible.[77]
Analytical activities of data users
Users may have particular data points of interest within a data set, as opposed to the general messaging outlined above. Such low-level user analytic activities are presented in the following table. The taxonomy can also be organized by three poles of activities: retrieving values, finding data points, and arranging data points.[78][79][80][81]
| # | Task | General Description | Pro Forma Abstract | Examples |
|---|---|---|---|---|
| 1 | Retrieve Value | Given a set of specific cases, find attributes of those cases. | What are the values of attributes {X, Y, Z, …} in the data cases {A, B, C, …}? | – What is the mileage per gallon of the Ford Mondeo?– How long is the movie Gone with the Wind? |
| 2 | Filter | Given some concrete conditions on attribute values, find data cases satisfying those conditions. | Which data cases satisfy conditions {A, B, C…}? | – What Kellogg’s cereals have high fiber?– What comedies have won awards?– Which funds underperformed the SP-500? |
| 3 | Compute Derived Value | Given a set of data cases, compute an aggregate numeric representation of those data cases. | What is the value of aggregation function F over a given set S of data cases? | – What is the average calorie content of Post cereals?– What is the gross income of all stores combined?– How many manufacturers of cars are there? |
| 4 | Find Extremum | Find data cases possessing an extreme value of an attribute over its range within the data set. | What are the top/bottom N data cases with respect to attribute A? | – What is the car with the highest MPG?– What director/film has won the most awards?– What Marvel Studios film has the most recent release date? |
| 5 | Sort | Given a set of data cases, rank them according to some ordinal metric. | What is the sorted order of a set S of data cases according to their value of attribute A? | – Order the cars by weight.– Rank the cereals by calories. |
| 6 | Determine Range | Given a set of data cases and an attribute of interest, find the span of values within the set. | What is the range of values of attribute A in a set S of data cases? | – What is the range of film lengths?– What is the range of car horsepowers?– What actresses are in the data set? |
| 7 | Characterize Distribution | Given a set of data cases and a quantitative attribute of interest, characterize the distribution of that attribute’s values over the set. | What is the distribution of values of attribute A in a set S of data cases? | – What is the distribution of carbohydrates in cereals?– What is the age distribution of shoppers? |
| 8 | Find Anomalies | Identify any anomalies within a given set of data cases with respect to a given relationship or expectation, e.g. statistical outliers. | Which data cases in a set S of data cases have unexpected/exceptional values? | – Are there exceptions to the relationship between horsepower and acceleration?– Are there any outliers in protein? |
| 9 | Cluster | Given a set of data cases, find clusters of similar attribute values. | Which data cases in a set S of data cases are similar in value for attributes {X, Y, Z, …}? | – Are there groups of cereals w/ similar fat/calories/sugar?– Is there a cluster of typical film lengths? |
| 10 | Correlate | Given a set of data cases and two attributes, determine useful relationships between the values of those attributes. | What is the correlation between attributes X and Y over a given set S of data cases? | – Is there a correlation between carbohydrates and fat?– Is there a correlation between country of origin and MPG?– Do different genders have a preferred payment method?– Is there a trend of increasing film length over the years? |
| 11 | Contextualization[81] | Given a set of data cases, find contextual relevancy of the data to the users. | Which data cases in a set S of data cases are relevant to the current users’ context? | – Are there groups of restaurants that have foods based on my current caloric intake? |
