Data science<\/strong><\/em> is called the 21^{st<\/sup> century\u2019s sexiest job. With the plethora of data<\/strong> that gets created today, it has become important for businesses and organizations to draw useful insights from them, which can help them make data-driven decisions. According to a report by DOMO, every single day, more than 2.5 quintillion bytes of data <\/strong>get created<\/span>. This is set to grow as by 2020, 1.7MB of data <\/strong>is estimated to be created each second for each person on earth. Thus, there\u2019s no shortage of data out there. However, getting the ability to access it and leverage it by acting on it – wherever, whenever, and however you need, is a different ballgame altogether. And since data science <\/strong><\/em><\/a>facilitates this, it has emerged as one of the hot dip topics <\/strong>in the modern era.<\/p>\n}

There\u2019s a plethora of data that gets generated every second. This includes your social media activities that trigger data creation in massive amounts, your communication modes (sending text or emails, making Skype calls, sending GIFs via Facebook messenger, Tinder swipes, etc.), your online purchases, the digital photos stored on your smartphones, or the statistics generated by businesses and service providers (such as trip details on Uber, songs added on Spotify, peer-to-peer transactions processed by Venmo, page edits made on Wikipedia, etc.), among others. Sitting on this stockpile of data<\/strong> won\u2019t help businesses unless they can spot trends, predict future demands, and leverage such insights to make proactive business decisions. Since data science professionals <\/strong><\/em><\/a>can help make sense of such data<\/strong> \u2013 be it structured and unstructured, there\u2019s a huge demand for them across industries and verticals.<\/p>\n

Perhaps this explains why there\u2019s almost a mad rush to get enrolled in data science training<\/strong> courses. Despite the domain being a much coveted one, especially as it offers high-paying jobs, you should know that it won\u2019t be a cakewalk to learn. So, if you perceive it to be an easy road, you should think twice before joining a data science training<\/strong> program. If you thought it would just need jumping on a data science training<\/strong> session to learn and basics and then become an expert, you are far from reality. You should be ready to work hard and remember that the most important thing you need to succeed in this field is depth, not just breadth. What this means is once you got the basics right, you need to choose your favorite domains and specialize. For example, some data scientists decide to delve deeper into operations and learn to tweak Apache Spark (the analytics engine) in detail.<\/p>\n

All these shouldn\u2019t sound discouraging because if you are really passionate about data science <\/strong>and are ready to invest adequate time and effort to learn and master some hot dip topics, you should definitely go ahead and join a suitable course that has the modules or course content to meet your learning and career goals.<\/p>\n

If the data science<\/strong> landscape sounds fascinating to you and you plan to get enrolled into some data science training<\/strong> courses or bootcamps, here are some hot dip topics<\/strong> that you should aim to learn and master.<\/p>\n

1- Fundamentals of Statistics<\/strong><\/h3>\n

<\/p>\n

Knowing<\/em> the concepts of statistics is the key to being a data scientist<\/em> or data analyst<\/strong><\/em><\/a>, irrespective of the language or tool you decide to use in the process.<\/span> If you have a Mathematics or Statistics background, you should join a data science training<\/strong> program that helps you brush up on your knowledge. But if you aren\u2019t from either of these fields, you should opt for a data science training<\/strong> course that covers topics such as probability, descriptive statistics, distributions, hypothesis testing, estimation, inference, regression, etc.<\/p>\n

Once you have learnt the basics, you should proceed to advanced tools, which could include the following:<\/p>\n

\n
• Probability value<\/li>\n
• General linear model<\/li>\n
• Central Limit Theorem<\/li>\n
• Time Series Analysis<\/li>\n
• Under and over Sampling<\/li>\n<\/ul>\n

  You should also learn about statistical methods such as Z\/t-tests (independent, one sample, paired), correlations and Chi-square, Anova (analysis of variance), etc. Additionally, your data science training<\/strong> course should teach you the important modules for statistical methods such as NumPy, Pandas<\/strong><\/em><\/a>, SciPy, etc.<\/p>\n

  To learn and master hot dip topics<\/strong> related to data science<\/strong>, having adequate knowledge of statistics is a must.<\/p>\n

  2- Python<\/strong><\/h3>\n

  <\/p>\n

  This<\/em> is one of the hot dip topics <\/strong>in the domain of data science. <\/strong>If you don\u2019t have any programming background, you should take up data science training<\/strong> courses that teach you the basics of Python<\/strong><\/a>.<\/span><\/em><\/span> This would usually include getting familiar with the data<\/strong> types, basic programming syntax, and programming building blocks using Python. Even if you are a programmer, who has been using other languages, learning the basics of Python at first would be a good choice.<\/p>\n

  Usually, data science training<\/strong><\/em><\/a> modules aiming to teach the essentials of Python will cover:<\/p>\n

  \n
  • An overview of Python followed by its installation<\/li>\n
  • Getting started with Python Editors & IDE’s (PyCharm, Canopy, Jupyter, IPython, Rodeo, etc.)<\/li>\n
  • Understanding the concept of Libraries\/Packages and learning about important packages (such as NumPy, SciPy, scikit-learn, Matplotlib, Pandas, etc.)<\/li>\n
  • Learning about data objects\/structures (strings, sets, tuples, lists, dictionaries) as well as data types<\/li>\n
  • Dictionary and list comprehensions<\/li>\n
  • Value labels and variables \u2013 time and date values<\/li>\n
  • Reading and writing data<\/strong><\/li>\n
  • Understanding the basic operations – Mathematical \u2013 date – string<\/li>\n
  • Control flow statements and conditional statements<\/li>\n
  • Simple plotting<\/li>\n
  • Code profiling and debugging<\/li>\n<\/ul>\n

    In case you already have a rock solid knowledge of the basics of Python, you can opt for advanced modules to data science<\/strong> with Python, which could include<\/p>\n

    \n
    • Scientific distributions uses in Python for data science<\/strong> such as NumPy, SciPy, Pandas, scikit-learn, NLTK, statsmodels, etc.<\/li>\n
    • Importing\/accessing and exporting data<\/strong> using Python modules<\/li>\n
    • Using Python modules for data <\/strong>cleansing, manipulation, and munging<\/li>\n
    • Using Python modules for data <\/strong>analysis and visualization<\/li>\n<\/ul>\n

      If you are wondering why you should learn data science<\/strong> with Python, the answer lies in the advantages this programming language brings your way. Some of these are:<\/p>\n

      \n
      • Python is an open source programming language.<\/span> Thus, this powerful programming language is free to use and has a robust community that works continuously to make it better and offers help to those who need it.<\/li>\n
      • This versatile and powerful programming language supports functional programming, structured programming, and OOP (Object-Oriented Programming) patterns.<\/li>\n
      • The Python Package Index boasts of about 72,000 libraries that can lend a helping hand in machine learning applications and scientific calculations.<\/li>\n
      • Python\u2019s syntax is easy to understand and readable, which ensures a reduction in development time by almost 50% when compared to other programming languages.<\/li>\n
      • With Python, you will be able to perform data visualization, data manipulation, and data analysis \u2013 all of which are extremely vital in data science<\/strong>.<\/li>\n<\/ul>\n

        Perhaps you now understand Python has emerged as the ideal and most preferred programming language to be used for data science.<\/p>\n

        3- R<\/strong><\/h3>\n

        <\/p>\n

        When<\/em> you consider hot dip topics <\/strong>in the field of data science, <\/strong>there\u2019s often a tussle between Python and R. If you wonder why you need to learn R<\/em><\/strong><\/a> for data science, <\/strong>here are some reasons:<\/p>\n

        \n
        • It\u2019s ideal for statistical analysis. Since R was originally devised by statisticians for performing statistical analysis, most statisticians still prefer it as their programming choice. With R\u2019s syntax, you can easily build complex statistical models with only a few lines of code. Additionally, since several statisticians use R packages and make their contributions to it, you\u2019re likely to get support for any statistical analysis that you may need to perform.<\/li>\n
        • Thanks to R\u2019s streamlined syntax for generating visualizations, you can quickly and easily build charts for exploratory data visualization.<\/li>\n<\/ul>\n

          If you are new to R, you should take up data science training<\/strong> courses that teach you the basics of R in relation to data science such as<\/p>\n

          \n
          • Basic knowledge of business analytics, common terms in analytics, analytics vs. data <\/strong>warehousing, MIS Reporting, OLAP, etc.<\/li>\n
          • Understanding how to install R-studio, R, and workspace setup.<\/li>\n
          • Knowledge of how different statements are executed in R.<\/li>\n
          • Knowledge of data structure<\/strong> used in R and ways to export\/import data<\/strong> in R.<\/li>\n<\/ul>\n

            Additionally, you will need to learn about DPLYP functions; use of various graphics in R for data visualization; use of classification techniques and linear, non-linear regression models for data analysis; ways to use different association rules as well as Apriori algorithm; use of clustering methods such as DBSCAN, K-means, and hierarchical clustering; implementation of Random Forest, Decision Trees, and Naive Bayes, etc; learn ensemble methods based on SVM, NN; text analytics with R as well as time series analysis with R.<\/p>\n

            Final words<\/strong><\/h3>\n

            <\/p>\n

            You<\/em> can find several data science training<\/strong> courses that cover these and some other hot dip topics <\/strong>in the domain. If you are ready to slog it out, you may even opt for data science <\/strong>bootcamps that are intensive, industry-relevant programs designed to run for a couple of weeks and cover a lot of relevant subjects. The key is to choose data science training courses<\/strong><\/em><\/a> that meet your learning and\/or career objectives and are run by reputed organizations that have well-experienced trainers or people from the industry to help students focus on what\u2019s really needed while avoiding the unnecessary details.<\/span><\/p>\n