Data Science is one of the most trending and upcoming niches in the technology industry today. The discipline uses modern tools and techniques to derive meaningful insights and hidden patterns from raw data of any format, structured or unstructured. Complex Machine Learning algorithms are used to learn and build predictive models. Data is the biggest asset of any organization. However, data would be of no use when left to remain as such. Therefore analyzing it to yield benefits is imperative for the success of any business. A Data Science course will help you master the core concepts of this field, such as data visualization, Machine Learning, data manipulation, web scraping, predictive modeling, etc.

Data Science projects have a few stages, they are:

  1. Problem discovery: The first step in a Data Science project is to identify the business problem. Proper business understanding is what will take the organization forward and way ahead of competitors. This is done by asking questions and locate the issue the business is facing. The team will formulate the hypotheses, which will later be tested with the data.
  2. Data collection: The next step is to obtain the data that you need to work on. You need to find information from various resources using query languages. The data can be of any format. To get this step done, you will need to be familiar with technical skills such as MySQL, PostgreSQL or MongoDB, Apache Hadoop, Spark, or Flink. Another alternative to acquiring data is from the files by downloading Kaggle or from any of the previously existent sources in formats such as Tab Separated Values (TSV) or Comma Separated Value (CSV).
  3. Data scrubbing: Data scrubbing is the next step in the process, and it simply means cleaning and filtering data. This is one of the essential steps in the whole process. Here, you need to convert the data and standardize it across the entire dataset into one for Data scientists will eliminate unwanted data, replace wrong values and even add missing values to create a useful dataset. Python and R are two programming languages that help in scripting. OpenRefine and SAS Enterprise Miner are two tools that can ease the scrubbing process for you. If you are dealing with massive datasets, Hadoop, Map Reduce, Spark, etc., should be beneficial.
  4. Exploratory Data Analysis: Exploratory Data Analysis or EDA is the step in which data scientists have to explore the cleaned data and examine its characteristics. Various statistical methods are used to execute this task, and the findings are visualized using data visualization tools to get a better picture of the results. Numpy, Matplotlib, Pandas or Scipy, GGplot2, the data exploration swiss knife Dplyr, etc., are standard technologies that help with EDA.
  5. Data modeling: Data modeling is another crucial step in a Data Science project. Before getting into the actual model building, professionals need to get rid of the features and characteristics that do not add any value to the project. Data scientists will carefully keep those elements that are advantageous and helpful in creating an accurate predictive model. After elimination, complex Machine Learning algorithms are applied to the data and trained for building models. Tools such as prediction and regression, Sci-kit Learn, CARET, and supervised and unsupervised algorithms are utilized during this step.
  6. Data interpretation: A model is measured according to its capability to predict and address the issues that might ensue in the future, and the capability of a model is conveyed during this step. Data interpretation is the final step in any project associated with Data Science, and it is the representation of your findings in the best possible way to business stakeholders. Your conclusions should be able to convince them to make improved decisions and thereby accelerate the growth. Matplotlib, ggplot, Seaborn, Tableau, d3js are a few technical skills you need to be competent in. Strong business acumen and soft skills such as presentation, writing, and communication, will also help you get a bigger picture of the process.

Being a certified professional in any field will enable you to march ahead of the other candidates right from the interview process. The best Data Science training in California will transform you into advanced coders and give you the chance to launch an excellent career in this innovative field. What precisely are the reasons to get certified in Data Science, and how is Data Science relevant in 2021?

  • Data Science is a domain in high demand these days and thus has a high capability of being the most employed field of science shortly. Enterprises, big or small, are now leveraging this technology and are being benefitted considerably.
  • It is a highly flexible industry, and candidates get the opportunity to get placed in various fields like retail, medicine, banking, finance, construction, transportation, communications, Media, and Entertainment, education, education, Manufacturing, Natural Resources, government systems, Energy and Utilities, and the Outsourcing Industry.
  • Although Data Science is remarkably popular and is versatile, the number of professionals available is far lesser than the desirable figure. There are numerous positions left to be filled in, and getting certified in this science will give you the potential to apply to the vacant positions.
  • It is a user-friendly domain as it helps to automatize a lot of repetitive tasks. Many companies are using Data Science to train models that will help mechanize redundant tasks and thus take the load off, humans.
  • The Machine Learning algorithms used in Data Science have helped companies build relevant and intelligent products for their consumers. Various tools and strategies are used to analyze data like the history of user encounters, giving information to the organization as to what kind of products users are looking for and creating likewise.
  • Organizations rely on data scientists for insights and information on improving their business growth. Therefore, this job role is considered the most important and professionals with the designation as a crucial part of the entire company.
  • The pay scale associated with this field is the most attractive factor for candidates. Data Science is a lucrative option for a career as they are paid much higher than all the other IT sectors. Getting certified in it will open you to opportunities in distinguished firms like Accenture, Google, Amazon, Apple, and much more.

What are the prerequisites to enroll in Data Science training in California?

  • Machine Learning is a prime competency any candidate applying for a course in this field should possess. It is the pillar of Data Science and is used to model and train the datasets to yield maximum results without being explicitly programmed. Familiarity with various ML algorithms is a prerequisite for joining these courses.
  • Mathematical modeling is another area that candidates need to be acquainted with. Modeling is the process that helps in scenarios such as matching algorithms with the problem, making predictions based on historical data, and handling calculations.
  • Data Science aspirants should be proficient in programming languages. Python is the most widespread language used in this industry since it can be easily incorporated with algorithms. R is also used in Data Science projects.
  • Database awareness is another prerequisite, and SQL is the most used query language in the field. You should be familiar with working, management, and extraction using the various databases.
  • Statistical knowledge is an expertise that will help you gain improved insights and derive better results from the data and trained models.

So, do you want to level up your skills in this efficient technology? Register now at SynergisticIT, one of the best online Data Science Bootcamps in California, and experience a potential breakthrough in your career