Data science for Python Programming
What Is Data Science?
Data science can be defined as a blend of mathematics, business acumen, tools, algorithms and machine learning techniques, all of which help us in finding out the hidden insights or patterns from raw data which can be of major use in the formation of big business decisions. In data science, one deals with both structured and unstructured data. The algorithms also involve predictive analytics in them. Thus, data science is all about the present and future. That is, finding out the trends based on historical data which can be useful for present decisions and finding patterns which can be modelled and can be used for predictions to see what things may look like in the future.
ANNEX Training institute Abu Dhabi offering Professional Training Course in Data science for Python programming by Industry Expert trainer With Flexible timing. We are providing Face to face Live Online Class or Class room training in our Center. After the successful Completion of the before issuing the Certificate there is an assessment test to evaluate your knowledge earned from theses course.
Why to Learn Data Science?With the amount of data that is being generated and the evolution in the field of Analytics, Data Science has turned out to be a necessity for companies. To make most out of their data, companies from all domains, be it Finance, Marketing, Retail, IT or Bank. All are looking for Data Scientists. This has led to a huge demand for Data Scientists all over the globe. With the kind of salary that a company has to offer and IBM is declaring it as trending job of 21st century, it is a lucrative job for many. This field is such that anyone from any background can make a career as a Data Scientist.
Exploratory Data Analysis and Data Visualization
Section 1: Numpy and Pandas
Accessing entries, saving and uploading numpy arrays
Series, Dataframes, combining dataframes
Saving and Loading dataframes
Section 2: Data Visualization using Python
Matplotlib and seaborn
Plots and graphs
Section 3: Exploratory Data Analysis
Data, datatypes and variables
Central Tendency and Dispersion
Skewness, covariance, coefficient of correlation
Univariate and multivariate analysis
Encoding categorical data
Working with outliers
Section 4: Project Hands on
Course Duration is Minimum 40 Hours.
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