#day66 of #66daysofdata R2D66 Watched videos on #Data Warehousing basics and started the below book on Snowflake. Will be having a break for now and finish up on the pending books and masters' assignments. Thanks @KenJee_DS for the initiative. Learned a lot during the 2 rounds.

#day65 of #66daysofdata R2D65 Finished inferential #statistics. Studied about Central Limit Theorem, confidence intervals and did some exercises related to it. Will start hypothesis testing afterwards.

#day64 of #66daysofdata R2D64 Finished #linkedin learning course on business #analytics: Marketing data. Will also continue with inferential statistics as well. linkedin.com/learning/certif…

#day63 of #66daysofdata R2D63 Continued with inferential #statistics. Studied Binomial theorem and cumulative #Probability. Also started week 3 of the customer #Analytics program (predictive analytics and linear regression) on coursera.

#day62 of #66daysofdata R2D62 Continued with #Probability and inferential #statistics. Studied random variables and discrete probability distributions. Will continue with continuous distributions tomorrow.

#day61 of #66daysofdata R2D61 Continued with probability concepts for #statistics. Studied key terms, deterministic and random experiments.

#day60 of #66daysofdata R2D60 Finished week 2 of customer @analytics course on coursera. Also started probability theory. Will focus on mathematics this weekend.

#day59 of #66daysofdata R2D59 Finished a course related to people #analytics on #LinkedIn learning. Also continued with the Week 2 of customer analytics course on Coursera. linkedin.com/learning/certif…

#day58 of #66daysofdata R2D58 Started customer analytics course on coursera. Did week 1 and on week 2, studied what is descriptive analytics and how data for the same is collected (via survey or online internet communities) Course may help in current job profile as well.

#day57 of #66daysofdata R2D57 Started #artificialintelligence programming in #python book. Finished 2 chapters (70 pages out of 868 as per my reader). A pretty large book but will try to complete within March itself.

#day56 of #66daysofdata R2D56 Performed Exploratory #DataAnalysis (EDA) on a banking case study in #python. Found some good insights and created various visualizations using matplotlib and seaborn. Below are some of the graphs from the notebook (didn't add description here).

#day55 of #66daysofdata R2D55 started a project/case study on banking and loan services for exploratory #dataanalysis (EDA) in #python. Studied risk analytics and loan application approval patterns.

#day54 of #66daysofdata R2D54 on the last module of #sql course on udacity. Will try to finish it by tonight. Studied advanced topics including self joins and performance tuning in Sql.

#day53 of #66daysofdata R2D53 Continued with the #sql course on udacity. Studied lag, lead, rank and row_number functions. Feel they're still a bit complicated so will revise again on weekend as well. Also restarted the below book for a better grasp on sql's practical usage.

#day52 of #66daysofdata R2D52 Resumed @udacity #sql course, currently on window functions. Also read PostgreSQL documentation regarding the same: postgresql.org/docs/9.1/tuto…

#day51 of #66daysodata R2D51. Completed Exploratory #DataAnalysis (EDA) on the bank dataset in #python, studied bivariate and multivariate analysis along with some interview questions.

#day50 of #66daysofdata R2D50 finished #artificialintelligence for marketing course on #linkedin learning. Learned onboarding AI tips, how to be a valuable human providing insights to AI rather than being replaced by it and places where ai and ml shine. linkedin.com/learning/certif…

#day49 of #66daysofdata R2D49 Continued with Exploratory #DataAnalysis (EDA) in #python. Did Univariate Analysis on the dataset and studied about Categorical Ordered, unordered univariate analysis & statistics on numerical features.

#day48 of #66daysofdata R2D48 Continued with Exploratory #DataAnalysis (EDA). Performed Data Cleaning, imputing missing values, standardization and handling outliers in #python.