DEV Community: Joy Cheruto

The Ultimate Guide to Data Analytics

Joy Cheruto — Sun, 25 Aug 2024 18:08:28 +0000

Data analysis skills such as statistical analysis, machine learning, and data visualisation are increasingly becoming among the most sort after in the job market.Organisations are seeking to leverage data and technology to propel themselves to success.So in this article we look into what it takes to become a pro data analyst.
So what is data analysis and why is it important?
It is the process of collecting, cleansing, and interpreting data sets to answer questions and solve problems for a business which helps ground business decisions with empirical data, allowing decisions to be made based on real world evidence. By extracting insights and deriving knowledge from data, businesses can enhance their decision-making processes.It is a great tool in predicting future trends.
What tools do you need as a data analyst?
You need mastery of these Top Data Analytics Tools:

Tableau
Excel or Google Sheets
SQL
Power BI
Python or R Types of Data Analysis Descriptive Analysis

This is a simple type of analysis that is not so in-depth. It looks at what has happened in the past. It includes two main processes: data aggregation, which includes gathering the data, and data mining, which includes discovering patterns in the data. It is simply an analysis that just determines and describes the "what.”

_Diagnostic Analysis
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This type of analysis explores the “why”. This type of analysis is where data analysts try to investigate the cause of why something has happened. If there is a drop in sales in September from a high in August, the analyst will try to figure out why such a thing has happened. Relationships are uncovered in this stage of the analysis.

_Predictive Analysis
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Just like the name suggests, predictive analysis tries to predict what is likely to happen in the future. In this type of analysis, data analysts start to come up with actionable, data-driven insights that the company uses to inform their next steps. It eliminates guesswork from key business decisions as future outcomes are based on historical data.

Prescriptive Analysis

Prescriptive analysis advises on the actions and decisions that should be taken. It is built upon predictive analysis, as it shows how a business can take advantage of the outcomes that have been predicted. It is very complex and sometimes even involves the use of computational modeling procedures and even machine learning.

Understanding and applying these methods and techniques empower data analysts to derive valuable insights, make informed decisions, and contribute to the strategic goals of organizations across diverse industries.

Understanding Your Data: The Essentials of Exploratory Data Analysis (EDA)

Joy Cheruto — Sun, 11 Aug 2024 15:02:49 +0000

Data science has become one of the fastest-growing fields with huge demands for skilled data scientists. Exploratory Data Analysis(EDA) is a popular method for analyzing and presenting data sets used by these professionals. Developed as the most comprehensive data analysis technique for data science projects, EDA has effectively contributed to providing maximum insight into the data set and data structures.
EDA is nothing but a data exploration technique to understand the various aspects of the data. It includes several techniques in a sequence that we have to follow.

The phases of exploratory data analysis can be summarized in 7 steps :

Know which problem area you will be covering and which questions you would answer.
Get a general idea of the dataset.
Define the types of data you have.
Choose the type of descriptive statistic.
Visualize the data.
Analyze the possible interactions between the variables of the dataset.
Draw some conclusions from all this analysis.

Exploratory Data Analysis Tools

Python – Python is an object-oriented programming language with high-level, built-in data structures. Other features like dynamic typing and dynamic binding work in favor of EDA. Python is extensively used to connect existing components and identify missing values in a data set.
Matplotlib – Matplotlib is one of the most widely used in data science for all kinds of graphics, such as bar charts, scatter charts, fever charts, and maps with Basemap, etc. Seaborn, another Python library based on Matplotlib, enables data scientists to create explanatory graphs from highly complex data.
R – R is an open-source programming language in statistical computing and graphics. It has a wide range of applicability in statistical observations and data analysis.
ggplot2 – ggplot2 is a library that allows bar, point, line, area, maps, and scale charts. ggplot2 depends on other packages that need to be downloaded and installed.

Importance of EDA

Data Cleaning: Identifying and handling missing values, outliers, and inconsistencies.
Feature Engineering: Creating new variables or transforming existing ones.
Model Selection: Choosing appropriate models based on data characteristics.
Insight Generation: Discovering hidden patterns and trends

By efficiently conducting EDA, you provide a solid basis for your data analysis journey. It enables you to discover the story concealed in your data and make informed decisions.

Expert advice on how to build a successful career in data science, including tips on education, skills, and job searching.

Joy Cheruto — Sun, 04 Aug 2024 16:19:24 +0000

Consider a society in which knowledge is the key to prosperity and individuals with access to it may reveal the future's mysteries. Greetings from the exciting world of data science, where creativity and curiosity converge to produce insights that can be put to use. A successful career in data science requires more than just knowing algorithms and crunching numbers in an increasingly information-driven world. It also requires embracing a journey of ongoing learning and strategic growth. With professional guidance, necessary skills, and job search tactics, this complete guide will help you navigate your journey into the field of data science.
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1.Educational Foundations

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The first way to achieve this is to get a bachelor’s degree in a relevant field such as data science, statistics, or computer science. It is one of the most common criteria companies look at for hiring data scientists. Although overtime the industry is gradually curving to accommodate the self-taught data scientist who have used online resources to keep up with the newest methods and trends in data science, embrace blogs, open-source initiatives, and online tutorials. Platforms such as Kaggle offer real-world experience through datasets and tournaments.
Another way is to enroll for data science boot camps as a way to brush up on relevant programming languages such as Python, R, SQL, and SAS. These are essential languages when it comes to working with large datasets.Which brings us to the second part of this article; skills.
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2. Developing Essential Skills

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In addition to different languages, a Data Scientist should also have knowledge of working with a few tools for Data Visualization, Machine Learning, and Big Data. When working with big datasets, it is crucial to know how to handle large datasets and clean, sort, and analyze them. Learn how to use tools such as Tableau, Power BI, or Python libraries like Matplotlib and Seaborn which are essential for presenting data insights in a clear and compelling manner.
Proficiency essential in programming languages such as R and Python. Python's abundance of libraries, like scikit-learn, Pandas, and NumPy, and its ease of use make it a popular choice. For statistical analysis and visualization, R is useful.
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3. Job Searching.

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Internships are a great way to get your foot in the door to companies hiring data scientists. Seek jobs that include keywords such as data analyst, business intelligence analyst, statistician, or data engineer. Internships are also a great way to learn hands-on what exactly the job with entail.
Once your internship period is over, you can either join in the same company (if they are hiring), or you can start looking for entry-level positions for data scientists, data analysts, data engineers. From there you can gain experience and work up the ladder as you expand your knowledge and skills.
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Conclusion

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A combination of formal education, ongoing learning, and real-world experience are needed to build a successful career in data science. You may position yourself for success in this fascinating and constantly changing sector by concentrating on growing your technical and soft abilities, building a solid portfolio, and making smart job market decisions. Take advantage of the chances and challenges that present themselves, and allow your enthusiasm for data propel you toward your professional objectives.