Los Angeles, California is home to Institutes like University of California that offers Master’s in Data Science. This degree can help you understand the basic concepts of Data Science and learn about all the technical skills required to become a Data Scientist. The top skills that are needed to become a data scientist include the following:
- Basic tools
- Statistics
- Software engineering
- Machine Learning
- Data Cleaning
- Data Munging
- Data Visualization
- Unstructured data
1. Basic tools: You must have a knowledge of statistical programming like R, or Python. Solving a problem in data science involves data preprocessing, data preservation, analysis, visualization, and predictions. Python has dedicated libraries such as – Pandas, Numpy, Matplotlib, SciPy, sci-kit-learn, etc. in order to perform these functions. In addition to these, advanced Python libraries such as Tensorflow, Pytorch, and Keras provide Deep Learning tools for Data Scientists. R ideal for not just statistical analysis but also for neural networks. In order to be a proficient Data Scientist, it is necessary to extract and operate on data from the database. Therefore, knowledge of SQL is a must. SQL is also a highly readable language, owing to its declarative syntax and variety of implementations.
2. Statistics: Data analysis requires descriptive statistics and probability theory which helps to make better business decisions from data. Key concepts include:
- Probability distributions
- Statistical significance
- Hypothesis testing
- Regression
3. Software engineering: Data scientists can gain huge benefits by learning concepts from the field of software engineering. It allows them to more easily reutilize their code and algorithms, and share it with collaborators. The important concepts include:
- Modularity
- Documentation
- Automation testing
4. Machine Learning: Machine Learning is a subset of Artificial Intelligence that provides systems the ability to automatically learn and improve from experience that is, the data collected over time and analyzed, without being explicitly programmed. It focuses on making future predictions from data available from past experiences. The data scientist feeds good quality data and then train our machines by building machine learning models using the data and different algorithms which depends on what type of data do we have and what kind of task we are trying to automate. Some machine learning methods are as follows:
- Supervised machine learning algorithms
- Unsupervised machine learning algorithms
- Semi-supervised machine learning algorithms
- Reinforcement machine learning algorithms
5. Data Cleaning: Altering and filtering data in such a way that it makes sense is called data cleaning. The general sequential steps to follow are given below:
- Remove duplicate and irrelevant information from your dataset
- Fix structural errors
- Filter unwanted data
- Handle missing data
- Checkpoint back the datasets
The tools available to help you with data cleaning are as follows:
- Trifacta,
- WinPure
- Alteryx,
- OpenRefine,
- Data Ladder,
- Paxata,
6. Data Munging: Data munging, also called data wrangling is the process of mapping the raw data into another format to make it more appropriate and valuable for a variety of downstream purposes such as analytics. The purpose of data wrangling is as follows:
- It should provide brief and workable data to Business Analysts
- Reduce the time and effort spent on collecting and arranging data
- Reduce the efforts of Data Scientist so that they can focus mainly on analysis rather than wrangling of data
- Drive better decisions based on data in a short time span
The tools available to perform data munging are as follows:
- Tabula
- DataWrangler
- OpenRefine
- Python and Pandas
- CSVKit
- “R” packages
- Mr. Data Converter
7. Data visualization: Data visualization methods are an important part of analytics which helps to quickly understand complex data. The process involves the creation of graphical representations of information by utilizing complex sets of numerical or factual figures. The essential data visualization techniques are as follows:
- Know Your Audience
- Set Your Goals
- Choose The Right Chart Type
- Number charts
- Maps
- Pie charts
- Gauge charts
- Take Advantage Of Color Theory
- Handle Your Big Data
- Use Ordering, Layout, And Hierarchy To Prioritize
- Utilize Word Clouds And Network Diagrams
- Include Comparisons
Common types of data visualization are as follows:
- Line charts
- Area charts
- Bar charts
- Population pyramids
- Pie charts
- Treemap
- Scatter plot
- Histograms
- Box plots
- Bubble charts
- Heat maps
- Choropleth
- Sankey diagram
- Network diagram
8. Unstructured Data: Unstructured data refers to data that does not follow any order like spreadsheet pages, database tables or other linear or ordered data sets and hence does not fit into the row and column structure of a relational database. Non-textual unstructured data such as MP3 audio files, JPEG images, and Flash video files, etc. and textual data including Word documents, PowerPoint presentations, instant messages, collaboration software, documents, books, social media posts, and medical records are all examples of unstructured data. This has led to a demand for new skills to handle such data, like NoSQL. Some of the tools for the analysis of unstructured data are as follows:
- MongoDB
- Cogito Semantic Technology
- Microsoft HDInsight