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Data analytics has become an integral part of the modern workforce, with its widespread use in healthcare, financial services, manufacturing, logistics, etc. This field is practical and requires extensive hands-on and conceptual training to become a prominent figure in this industry. The data analytics portfolio projects have the potential to set you apart as a data analyst, highlighting your skill sets to recruiters. If you are new to data analytics, then there is also an option for beginner data analytics projects. In this article, we will provide you with numerous project ideas to get started with.

1. Exploratory Data Analysis on Public Data- It is the first step of any data analysis process. It enables young aspirants a better understanding of the data sets, before performing further analysis. In this project, learners start with choosing a public data set like those available on the Kaggle, or UCI Machine Learning Repository, and executing EDA on it. The steps to execute these simple data analytics projects include

• Charging the data sets using programming languages like Python, R, etc.
• Processing the data sets for missing, and erroneous values.
• Execute summary statistics
• Imaging the data sets via infographics such as histograms, box plots, scatter plots, etc. 
• Produce insights about the relationships between variables.

The prime tools used in this project are Pandas, NumPy, Matplotlib, Seaborn (Python libraries), and ggplot3, dplyr (R libraries).

2. Sales Dashboard Creation Using PowerBI or Tableau- It is considered among the entry-level data analyst projects. The ability to develop an appealing dashboard is a valuable proficiency for data analysts. This is because the stakeholders of this business don’t need to have technical knowledge. Thus, presenting the business-related information (obtained from the data sets) in a visually appealing manner for easier understanding. In this project, individuals need to showcase a dashboard displaying sales profiles across diverse locations, periods, and product categories. The steps of this project are as follows

• Choosing the appropriate sales data (can be of the organization or from open-source platforms).
• Feeding the data sets into PowerBI and Tableau.
• Developing charts like bar graphs, line charts, pie charts, etc.
• Integrate communication with filters for users to explore data based on time and region.
• Publish your dashboard for public and stakeholder applications.

PowerBI and Tableau are the two main tools used for performing this project.

3. Movie Suggestion System Using Collaborative Filtering- Creating a suggestion system is a mindblowing introduction to the principles of machine learning and predictive analytics. It is one of the most common hands-on data analytics projects, which can be easily understood and implemented. In this case, the data analytics aspirants will use collaborative filtering, which you might have seen often applied by companies like Netflix and Amazon to suggest content or products. The steps for carrying out this project are

• Application of data sets that comprise user ratings for movies.
• Cleaning the data sets for all the inconsistencies, and outliers and producing a user-item matrix.
• Implementing collaborative filtering like matrix factorization to forecast how users will rate unseen movies.
• Assess the performance of your prototype implementing metrics such as mean absolute error (MAE).
• Imaging the top movie suggestions for users based on their preferences.

The most common tools that can be used for this project are Python  (Scikit-learn, Pandas) and R (recommenderlab). 

4. Sentiment Analysis on Product Reviews- This project involves the natural language processing technique. This technique talks about whether a text has a positive, neutral, or negative sentiment. It helps examine feedback or reviews. This project enables building a data analytics portfolio by giving robust expertise to explore text data and implement machine learning algorithms to segment sentiment. The steps to solve this project are

• Select a data set with customer reviews.
• The data sets should be free of any stop words, punctuation, and implementing tokenization.
• Change the text data into numerical format using techniques such as Bag of Words or TF-IDF.
• Create a machine-learning model (for example- logistic regression or Naive eyes) to classify the sentiment of each review. 
• Assess the model’s performance using precision, accuracy, recall, or F1 score.

The common tools that are used during these projects for data analytics beginners are Python (Scikit-learn, NLTK) and R (TM, and text2vec).

5. Time Series Forecasting on Stock Prices- These time series projects for data analytics beginners are used for predicting data change over time. Most finance sectors use this kind of model for predicting stock prices or sales over time. The steps for solving this project include

• Search for historical stock price data sets from the internet.
• Preprocess these data sets for any inconsistent values.
• Perform exploratory data analysis to identify trends, seasonality, and patterns in the stock price.
• Apply prototypes like ARIMA (Autoregressive Integrated Moving Average) to predict future stock prices.
• Assess the efficacy of the prototype using Root Mean Square Error (RMSE) or Mean Squared Error (MSE).

The tools used for executing this project are Python (stats models, pandas) and R (forecast).

Conclusion

Developing strong Data Analytics Portfolio Ideas needs hands-on experience and a strong theoretical understanding of data analytics. As you progress towards finishing these projects, it is very important to document each of your steps towards solving them carefully. This will need you to build a clear approach towards the project, identify target problems, and elucidate the tools you are going to use for their completion. These Data Analyst Projects to Build Portfolios are the most common ways to get familiar with the industry-oriented application of data analytics methodologies. As you advance, you can take up more complex projects to further improve your portfolio.