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Embarking on a Career in Data Analysis: Tips for Starting Your Journey

Feb 14th 2023 8 Min read

Data analytics refers to the process of collecting, processing, and analyzing large data sets to extract insights and identify trends. Data analytics is used in many industries, including healthcare, finance, marketing, and government. It involves a range of techniques, from basic data exploration and visualization to complex machine learning algorithms.

Data analytics involves several steps, including:

Data collection: Gathering data from various sources, including databases, websites, sensors, and other digital channels.

Data processing: Cleaning, organizing, and structuring the data to make it easier to analyze.

Data analysis:Using statistical and mathematical models to explore the data, identify patterns and trends, and develop insights.

Data visualization: Presenting the results of the analysis in a visual format, such as graphs, charts, and dashboards, to make it easier to understand and communicate.

Data analysis is the process of cleaning, changing, and processing raw data, and extracting actionable, relevant information that helps businesses make informed decisions. The procedure helps reduce the risks inherent in decision-making by providing useful insights and statistics, often presented in charts, images, tables, and graphs.

Types of Data Analysis

There are a half-dozen popular types of data analysis available today, commonly employed in the worlds of technology and business. They are:

Diagnostic Analysis: Diagnostic analysis answers the question, “Why did this happen?” Using insights gained from statistical analysis (more on that later!), analysts use diagnostic analysis to identify patterns in data. Ideally, the analysts find similar patterns that existed in the past, and consequently, use those solutions to resolve the present challenges hopefully.

Predictive Analysis: Predictive analysis answers the question, “What is most likely to happen?” By using patterns found in older data as well as current events, analysts predict future events. While there’s no such thing as 100 percent accurate forecasting, the odds improve if the analysts have plenty of detailed information and the discipline to research it thoroughly.

Prescriptive Analysis: Mix all the insights gained from the other data analysis types, and you have prescriptive analysis. Sometimes, an issue can’t be solved solely with one analysis type, and instead requires multiple insights.

Statistical Analysis: Statistical analysis answers the question, “What happened?” This analysis covers data collection, analysis, modeling, interpretation, and presentation using dashboards. The statistical analysis breaks down into two sub-categories:

Data Analysis Method

There are many data analysis methods available, they all fall into one of two primary types: QUALITATIVE and QUANTITATIVE ANALYSIS

Qualitative Data Analysis: The qualitative data analysis method derives data via words, symbols, pictures, and observations. This method doesn’t use statistics. The most common qualitative methods include:

Analysis, for analyzing behavioral and verbal data.

Narrative Analysis, for working with data culled from interviews, diaries, surveys.

Grounded Theory, for developing causal explanations of a given event by studying and extrapolating from one or more past cases.

Quantitative Data Analysis: Statistical data analysis methods collect raw data and process it into numerical data. Quantitative analysis methods include:

Hypotesis Testing, for assessing the truth of a given hypothesis or theory for a data set or demographic.

Mean, or average determines a subject’s overall trend by dividing the sum of a list of numbers by the number of items on the list.

Size Determination uses a small sample taken from a larger group of people and analyzed. The results gained are considered representative of the entire body

As you embark on your journey to become a data analyst, here are some tips and tricks to help you achieve your goal:

Start with the basics: Begin by learning the fundamentals of data analysis, including statistics, data visualization, and programming languages such as Python and R.

Learn by doing: The best way to learn data analysis is by applying your knowledge to real-world problems. Practice by working on projects and analyzing data sets.

Seek out resources: There are many resources available to help you learn data analysis, including online courses, books, and tutorials. Find the ones that work best for you.

Join a community: Join online communities, such as forums or social media groups, where you can connect with other data analysts and learn from their experiences.

Attend events: Attend conferences, meetups, or other events where you can network with other data analysts and learn about the latest trends and technologies in the field.

Keep up with the latest tools and techniques: Data analysis is a constantly evolving field, so it's important to stay up-to-date with the latest tools and techniques.

Practice good data hygiene: Always ensure that your data is clean, accurate, and relevant.

Stay curious and persistent: Don't be afraid to ask questions and seek help when needed. Data analysis can be challenging, but with persistence and dedication, you can achieve your goals.

There are several tools and technologies used in data analytics, including statistical software such as R and Python, data visualization tools like Tableau and Power BI,Excel and machine learning algorithms like deep learning and neural networks.

Remember, learning data analysis is a journey, not a destination. Stay committed, keep learning, and never stop exploring the vast and exciting world of data analysis.
Best of luck,

Osinakachi David Nwakire

I am a tech enthusiast skilled in web design, graphics, and data analysis. With over 200 successful students, I am also an experienced educator.