Data Science Assignment Tips

How to successfully write Data Science Assignment?

Where will I come across Data Science assignments?
As a college student, if you are pursuing a degree in Computer Science, Economics, MBA, Statistics, or any related field, you will be coming across the subject of Data Science. In simple terms, Data Science involves using various computational software to draw insights from a dataset. Understanding the data for making decisions is one of the prime characteristics expected from a Data Scientist.

Personal and Professional Skill Sets
As a student aspiring to write and solve a Data Science assignment successfully, one must develop some personal and professional skill sets. It would help if you had a curious mindset, ask yourself whether you are excited to know what this dataset speaks of. You should have the ability to question; this will immediately give you a context to analyze and framing your report. Your curious mind is essential for a good data science assignment. You have to pay heed to minute details, have clarity of understanding. Along with the knowledge in statistics, what is also required is your ability to communicate your results.

Getting Started with the Assignment
When you get an assignment in Data Science, the first thing that you should do is read the questions thoroughly. Some assignments might ask you to be creative; some may ask you to be very specific. Understanding what has been asked for is of utmost importance. This overview would set your path to solving the assignment. Right after this, you, as a student, should immediately recollect the concepts and methodologies that you might need to solve the questions. Drill down to specific concepts, and then finally decide the tool or software that you would like to use.

Before starting to solve the assignment, begin by exploring the dataset that has been given. We prefer using R. There are many packages in R that help you get an exploratory overview of the dataset delivered. Check for invalid data, missing data, and any outliers or errors present in the dataset. Correcting them before the assignment is very important. Understand the type of variables that have been used whether the data is a categorical, nominal, ratio scale, interval scale, ordinal. Understanding the kind of variables will help you in analysis and also visualization. You might want to try creating some basic visualizations as well before getting into the analysis.

Analyzing the dataset
Stick to the basic concepts and start solving the questions. One major question that arises in students’ minds is the clarity of which is the dependent variable and an independent variable. This typically becomes important while running regressions. Be through with standard statistical tests and how they are to be performed using the software. Pay extra attention to units of measurement of variables. Ordinal variables and coded variables typically become a bit complex during interpretation. Make sure to take care of this.
Communicating the results
Communicating your findings is of great importance. This means you have to pay heed to the interpretation of your data analysis results. Keep it short and to the point. Try connecting the interpretation with a write-up on how it is linked to the variables into consideration. This is where you might need some help initially from experts. The report writing contributes significantly to your marks. With experience comes the ability to join the dots between statistical results and real variable based analysis based on that. Make sure the report is well-formatted.

Challenges we may help you with
Students, at times, are unable to relate the theory and concepts with the dataset questions given. When exactly to go for a logit model, or which variables are to be used when you want to run a Chi-Square test in a dataset, is what experts can help you with. As you solve more questions, you will gain confidence. Report writing is another aspect where initially you might take help from experts. Once you get to rectify the errors you are making or see the examples of useful reports, you will eventually get the hang of what is expected of a data science assignment.

Your Future
Whether you solve it on your own or take help, Data Science assignments given at the college level is of great importance for a successful career ahead. So make sure to get it solved even if you alone cannot do it. It will be a game-changer directly or indirectly to your bright future.
So if you are still finding it difficult to adjust your skills to finish your assignments on time, we at Edumanta have a team of experts and educators who will guide you through your assignments. We assure you the best quality assignments and even better a fast delivery. Not just guiding you through solving assignments, but we also offer online tutoring 24*7 service. You will grace through with flying marks, gain knowledge, and eventually pave your way for a successful career ahead. Visit:

Python data mining

Your project proposal must be typed and should be approximately one page long. The purpose of the proposal is to help you sort and summarize your project ideas, and select your most interested data mining topic for project. We will review your project proposal and make sure you are on the right track. After submitting the project proposal, you will need to discuss with me to confirm and finalize your project topic and directions at Office Hours. We will give you project feedback comments so you can complete a high-quality data mining project.In your proposal you should cover the following items:• Tentative title of the project.• Abstract for your project topic. It should be one paragraph long, and should provide a high level summary of your project and outline your main goals. What is the major data mining problem and why it is meaningful to perform data mining on this data or topic?  • Brief description of project plan.1. What data sets do you plan to use? Describe the data briefly and provide the information of the data sources. We do not require significant effort on data collection and processing in this project. You can use data sets from UCI, Kaggle, or other public datasets on your interested topics, such as healthcare, energy, manufacturing, etc.  2. If you need do significant work to process raw data and convert it into the proper format for data mining. You can describe the expected data processing step.3. What programming languages do you plan to use (Matlab/Python/R)? What other machine learning tools do you also plan to use (e.g., WEKA, Tableau, SAS, etc. This is optional.)   4. How do you formulate the data mining problem? E.g., is it a classification task for discrete class labels, or a regression/prediction task for continuous response variables? You can also do both classification and regression on one dataset. For example, you can discretize continuous response variable into multiple categories (such as low, medium, high), then we can convert the problem into a classification problem, and implement classification models. 5. Note describe what exactly are you trying to predict or classify. It is critical that your problem is well-defined. 6. What data mining methods tentatively to be implemented for the project? (e.g., decision trees, KNN, Bayesian decision rules, LDA, neural networks, SVM, Neural Networks etc.) We would like you to practice different classification/prediction models on your project, and compare the performance of different models. This is just a draft plan, and you can add more models later when you make more progress on your project.  7. Indicate what types of projects you are going to do. Research project or application-based project. 
Types of ProjectsThere are two main types of projects. Research Project: you can decide to do a research project, where you look at a research issue. This could be original research, but could also be something straightforward—such as an empirical evaluation of data mining methods or strategies for improving performance (e.g., a study about strategies for removing missing values, evaluate different feature selection algorithms using simulated and real-world datasets, explore recent machine learning and deep learning methods on some research data). If you would like to do research project, we could provide some research dataset for you to explore. And also provide some new data mining ideas to explore. This option mainly applies to PhD students and senior MS students with good programming skills.Application Based Project: this is the most common project format and many of you will select application-based project to explore some real-world data sets using learned data mining models and methods. You can select something interesting for data mining, practice essential data mining steps, including data preprocessing, data visualization, variable selection (optional), classification/prediction modeling, model parameter tuning, and model performance evaluation. You should make sure that your analysis is not trivial, and explore some meaning data mining tasks. For example, running a data set through WEKA and spending an hour on the analysis and then doing a quick write-up would be considered trivial. You should study the dataset, determine the issues, address any preprocessing issues, try multiple modeling techniques, and perhaps take some creative steps to try to improve the classification or predictive performance.
Project ReportEach team will complete a data mining report at the end of the semester. It is very important for everyone to learn scientific writing for technical report. This is an important skill for your future work. The project report need be well organized and clearly written. The following report sections can be taken as a reasonable template for your project report writing.• Abstract: summarizes the project and the goals of the data mining work (required)• Introduction: Introduces the project and what you are trying to do. Also include relevant information to introduce the data mining problems and why it is a meaningful topic. What are the motivations people do data mining on this topic. • Background: you may want a separate background section to provide domain information for the topic that you are studying. You can describe with citations to relevant papers, documents, or web recourses. For public datasets on an interesting topic, you can always find a lot of related work. Assume you are writing a technical paper to public readers, you can introduce the domain knowledge and problem background information clearly to help readers understand the problem and the filed. You can also combine background and introduction into one section (with sub-sections).• Dataset Description: Describes the experiments and the experimental setup for data collection based on the documents from data recourses. Will describe the explored data sets in details. • Data Mining Experiments: in this section, describe data mining experiments you have done, such as data processing, feature extraction, feature selection, data mining models and tools, data mining strategies you explored, the evaluation metrics, and any other work related to the data mining experiments.• Experimental Results: summarize the experiment results of different models and methods/ideas. A discussion of the results may be included. • Conclusion: Provide your conclusion. For example, comment on the quality of your results. You may also want to include some material on future work, whether or not you intend to do such work. A high quality data mining project may generate a conference or journal paper after the class. • References: you may cite some papers and documents/website in the sections above. Make a reference list with clear index.