What Everyone Ought To Know About DATA SCIENCE COURSE

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The rise of Data Science as an emerging field is not new news to our ears. Data Science has been an evolving area of study considering the mentions the field has garnered to its name over these past few decades. While higher pay and many career opportunities may seem to attract the masses, but there is limited knowledge as to what we are exposed to about the inside details and pre-requisite knowledge. Let us learn about the many concepts covered under a general data science course:

Mathematics:

An aspirant is required to have a basic knowledge of Mathematics and Statistics. Data science courses, in turn, do not fail to revise these concepts. With convex optimization listed under optimization and Bayes Theorem included under the Probability heading. Chapters of Linear Algebra are inclusive of integration / integrals, differentiation and differential equations, etc.

Statistics:

Similar to the case of mathematics, aspirants are expected to have an understanding of basic topics in statistics. The concepts include range from statistical topics such as random variable to descriptive statistics. Other statistical concepts consist of chi-squared tests, inferential statistics, Gaussian distributions, and lastly normal distributions.

Data Visualization and Data Analysis:

It is no doubt that data visualization and data analysis as a whole prove to be a prominent share of the data science enterprise. Aspirants should know how to plot libraries in the programming languages, these comprise matplotlib, seaborn, plotly in Python. An additional knowledge of Excel, D3.js and Tableau is considered mandatory for data visualization in the interactive sense.

Programming Languages:

Several programming languages such as Python, R, C, C++, Matrix Laboratory (MATLAB), Octave GNU Linux, Julia and Go are considered mandatory as a base to go in for Data Science. Packages and libraries such as PandaSQL, Urllib, Seaborn, Matplotlib and Pandas are essential for learning Python. Likewise, ggplot2 is essential for the understanding of R.

Machine Learning (ML):

Among the many topics to be explored under machine learning, Testing, Reinforcement Learning, Validation and Evaluation of the models, and Supervised and unsupervised learning hold place. The aspirants will be able to grasp these concepts in the data science training.

Management of data and Data Munging:

Concepts and processes such as Data Mining, Management of data, Data Cleaning are made familiar to the students within the coaching period. The knowledge of structured query languages (SQL), for instance NOSQL, Cassandra, MySQL, MongoDB, etc.

Moreover, In-depth teaching of the following topics is taken into account:

  • Cross validation
  • Bayes Theorem
  • Naïve Bayes
  • Gradient Descent
  • Ensemble learning
  • Principal Component Analysis
  • Entropy
  • F1 Score
  • Collaborative Filtering
  • Ada Boost
  • Train-test
  • Precision
  • Recall
  • Linear Regression
  • Hierarchical Clustering
  • Support Vector Machines
  • Logistic Regression
  • Boosted Trees
  • Polynomial Regression

Deep Learning:

The topics falling under Deep learning are as follows:

  1. Neural Networks
  2. LSTM (Long Short Term Memory)
  3. Hyper parameters
  4. RNN (Recurrent Neural Nets)
  5. Feed-Forward Neural Networks
  6. Object Detection, etc.

Big Data

High-speed tools such as Spark, Hadoop, and Hive are taught along the lines of Big Data Chapters.

Resource Box

Do you have a liking that inclines towards Data Science? If so, then this is your chance! Hurry and confirm your seats at 360DigiTMG’s data science course certification training for a self-paced and efficient E-learning experience.

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360DigiTMG – Data Analytics, Data Science Course Training Hyderabad

2-56/2/19, 3rd floor,, Vijaya towers, near Meridian school,, Ayyappa Society Rd, Madhapur,, Hyderabad, Telangana 500081

Hattie B. Trosclair

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