"Data Science In Practice" is an intensive program designed to introduce learners to data science through tangible case studies and tangible tools such as Product Data Science, Machine Learning, and Recommender Systems.
The modern world thrives on data, posing the intricate challenge of its practical application. The transition from mere theoretical understanding to hands-on problem-solving in data science remains a daunting leap. Our directive is clear: mold learners into proficient data science practitioners.
Our ambition is to arm learners with first-hand experience across three pivotal sectors of data science. Upon course culmination, participants will have spearheaded three comprehensive projects, deftly applying Python and a gamut of data science techniques, positioning themselves as ready contenders in the job market.
The backbone of our curriculum is Python, supplemented with a suite of data science tools. We've integrated methodologies like Exploratory Data Analysis (EDA), Econometrics, Linear Regression, and pivotal Machine Learning algorithms such as Bagging, Random Forest, and XGBoost. Moreover, NLP methods like CountVectorizer are employed for textual analysis.
Our pedagogy is rooted in a meticulously structured journey that segments into four stages: Setting The Course, Python Programming, Data Science Analysis, and Portfolio Development. Each case study converges into actionable insights, empowering learners to not just comprehend but adeptly implement data science knowledge. A testament to our success, 97% of our graduates echo notable professional growth following the program.