Introduction

In the data-driven world there are different roles such as data engineer, data scientist, and data analyst. Here, the data analyst role will be discussed. Data Analysts are responsible for interpreting data, analyzing results, and providing actionable insights that drive business decisions. They use different analytical skills such as statistics, data mining algorithms and reporting and visualization to present the insights. Let’s explore what Data Analysts do, the tools they use, their essential skillsets, and some valuable resources and courses to kickstart or advance a career in data analysis.

What Does a Data Analyst Do?

The main job of data analysts is extracting meaningful insights from data. To do so, they should follow the following step do deliver the required values to the company.

• Understanding the problem: Their first step is to understand the problem. The problems are usually a business problem. For example, why our discount policies does not work? Why conversion rate of the online shop fell significantly in the previous week?
• Identify the required data: After understanding the problem, the required data for analysis should be identified. It is important that the data should be relevant and clean. So, the next step comes.
• Data Collection and Cleaning: Gathering data from various sources and ensuring its accuracy and consistency. The tools and programming languages that they use are SQL extracting from the data base and Python libraries such as NUMPY, or PANDAS to manipulate the extracted data and save them in an appropriate format.
• Data Analysis: Using statistical techniques and tools to analyze data and identify trends, patterns, and anomalies. To do the analysis, they can use python libraries such as SciPy, Numpy, ScikitLearn, and Tensorflow or R language. Note that While they usually do not use machine learning algorithm and they analyze the data as is, they use SciPy and NumPy more often.
• Reporting: Creating visualizations, dashboards, and reports to present findings to stakeholders. The visualizations can be made using Python or R libraries such as Matplotlib in python or using Power BI.
• Problem-Solving: Using the combination of statistical analysis and visualizations to solve present answer for the business problem.
• Presentation: Finally, they should present their findings to the stakeholders and managers.

Difference Between Data Analysts and Data Scientists

While Data Analysts and Data Scientists share some similarities, their roles and responsibilities differ in several ways:

• Focus: Data Analysts primarily focus on interpreting existing data and providing actionable insights for business decisions. Data Scientists, on the other hand, focus on developing advanced models and algorithms to predict future trends and uncover deeper insights.
• Tools and Techniques: Data Analysts often use SQL, Excel, and data visualization tools, while Data Scientists use more advanced programming languages like Python and R, and specialized libraries for machine learning and deep learning.
• Skillset: Data Scientists typically possess a stronger background in mathematics, statistics, computer science, and are skilled in advanced analytical techniques and machine learning algorithms.

Statistical Techniques Used by Data Analysts

The core knowledge needed for data analysis are statistics. In other words, Data Analysts do not anything other than sampling the data, visualization, finding and forecasting the populations’ behavior, and designing experiments. Some common skills that they need are:

• Descriptive Statistics: Summarizing and describing the main features of a dataset (e.g., mean, median, mode, standard deviation) and also use different charts to show the features of the dataset and population.
• Inferential Statistics: Making predictions or inferences about a population based on a sample of data (e.g., hypothesis testing, confidence intervals). For example, they may design an experiment (for example A/B testing for the effect of a particular feature of the site conversion rate of an online shop.)
• Regression Analysis: Understanding relationships between variables and predicting outcomes (e.g., linear regression, logistic regression).
• Correlation Analysis: Measuring the strength and direction of relationships between variables.
• Time Series Analysis: Analyzing data points collected or recorded at specific time intervals to identify trends and seasonal patterns.

Essential Skillset of Data Analysts

To excel in their role, Data Analysts must possess a diverse skillset that encompasses both technical and soft skills. Key skills include:

• Analytical Thinking: Ability to understand complex data sets and derive meaningful insights.
• Technical Proficiency: Strong knowledge of SQL, Excel, and data visualization tools.
• Statistical Knowledge: Understanding of statistical methods and their application in data analysis.
• Programming Skills: Proficiency in languages like Python or R for data manipulation and analysis.
• Communication Skills: Ability to clearly present findings and recommendations to non-technical stakeholders.
• Problem-Solving: Aptitude for identifying problems and developing data-driven solutions.
• Attention to Detail: Ensuring data accuracy and consistency throughout the analysis process.
• Domain Knowledge: Understanding the specific industry or business context to provide relevant and actionable insights.

Libraries Used by Data Analysts

Data Analysts often use several libraries in Python and R to facilitate data analysis and visualization:

• Pandas: A Python library for data manipulation and analysis, providing data structures like DataFrames.
• NumPy: A library for numerical computing in Python, offering support for arrays and matrices.
• Matplotlib and Seaborn: Python libraries for data visualization, used to create static, interactive, and animated visualizations.
• SciPy: A Python library used for scientific and technical computing.
• Scikit-learn: A Python library for machine learning, offering simple and efficient tools for data mining and data analysis.
• Statsmodels: A Python library that provides classes and functions for the estimation of statistical models

Courses and Resources

For those aspiring to become Data Analysts or looking to enhance their skills, there are numerous resources and courses available:

• Fundamentals of data visualization in Coursera: this course introduces stages of visualizing data to present meaningful insights. The stages starts from knowing the question to presenting the final visualization. Furthermore, it instructs about ethical aspects of data visualization and finally teaches how to evaluate final visualization and improve it.
• Google Data Analytics Professional Certificate: This is an 8 course specialization that starts from foundation of data analysis. Then instructs data preparation, cleaning and visualization. Furthermore, there is a course on R language for data analysts and finally a capstone project is the last course.
• Data Science Math Skills: This course introduces the basics of mathematics, from problem solving, to functions and graphs, and probability and statistics.
• Introduction to probability and data with R: This course introduces you to sampling and exploring data, as well as basic probability theory and Bayes’ rule. You will examine various types of sampling methods, and discuss how such methods can impact the scope of inference. A variety of exploratory data analysis techniques will be covered, including numeric summary statistics and basic data visualization. All of the concepts will be practiced in R language to make it more practical